Kun Ma  Kun Ma

LatestTOP

    Education

  • Kun Ma, Bo Yang, Jin Zhou, Yongzheng Lin, Kun Zhang, Ziqiang Yu, Outcome-based School-Enterprise Cooperative Software Engineering Training, Proceedings of 2018 ACM Turing Celebration Conference China (TURC 2018), Shanghai, China, May 19-20, 2018, 15-20 (计算机教育顶级会议，中国图灵大会。该论文被教育部高等学校计算机类专业教学指导委员会评为“第二届全国高等学校计算机教育教学青年教师优秀论文奖二等奖”。)
  • 马坤,蔺永政,韩士元,周劲,董吉文,杨波, "成果为导向多方共赢的校企协同创新创业实践方法与课程体系建设," 计算机教育, 2019, 21 (7): 102-106 (计算领域高质量科技期刊T2/CCF C类，本期封面文章，该论文被教育部高等学校计算机类专业教学指导委员会评为“2019-2020全国计算机教育优秀论文一等奖【本期参评的1006篇论文仅评选出3个一等奖】”。)
  • Kun Ma, Nan Zheng, Shan Jing, Zhenxiang Chen, Bo Yang, "TELF-PSPOC: Three-layer Ensemble Learning Framework for Predicting Student Performance of Online Courses," 计算机教育, 2022, 20 (12): 85-95(计算领域高质量科技期刊T2/CCF C类)
  • Kun Ma, Yongwei Shao, Jiaxuan Zhang, Zhenxiang Chen, Bo Yang, "ELM-EDP: Ensemble Learning Model FOR Early Dropout," 计算机教育, 2023, 21 (12): 124-139 (计算领域高质量科技期刊T2/CCF C类)
  • Kun Ma, Bo Yang, Kun Liu, Automated Grading of Collaborative Software Engineering Training with Cloud Distributing Scripts, Proceedings of 2019 ACM Turing Celebration Conference China (TURC 2019), Chengdu, China, May 17-19, 2019, No. 84(计算机教育顶级会议，中国图灵大会。)
  • Kun Ma, Kun Liu, and Lixin Du, Automated Assessment and Evaluation of Contribution of Collaborative Software Engineering Development Process, Proceedings of the 2020 27th Asia-Pacific Software Engineering Conference (APSEC 2020), Singapore, Singapore, Dec. 1-4, 2020, 500-504 (CCF C类会议)
  • Jiaxuan Zhang, Kun Ma*, EDPS: Early Dropout Prediction System of MOOC Courses, Proceedings of the 2022 29th Asia-Pacific Software Engineering Conference (APSEC 2022), Online, Dec. 6-9, 2022, 562-563 (CCF C类会议)

    • Research

    Book
  • Kun Ma, Ajith Abraham, Bo Yang, and Runyuan Sun, Intelligent Web Data Management: Software Architectures and Emerging Technologies, Springer International Publishing, Switzerland, ISBN-13: 978-3-319-30191-4, 162 pages, 2016.
    • Journal
  • Xinyu Liu, Kun Ma*, Qiang Wei, Ke Ji, Bo Yang, and Ajith Abraham, "G-HFIN: Graph-based Hierarchical Feature Integration Network for Propaganda Detection of We-media News Articles," Engineering Applications of Artificial Intelligence, 2024, 132 (6): 1-16 (EI: , WOS: IF: 7.802, CCF C类, Q2, Top期刊)
  • Qiang Wei, Kun Ma*, Xinyu Liu, Ke Ji, Bo Yang, and Ajith Abraham, "DIMN: Dual Integrated Matching Network for Multi-Choice Reading Comprehension," Engineering Applications of Artificial Intelligence, 2024, 130 (4): 1-11 (EI: , WOS: IF: 7.802, CCF C类, Q2, Top期刊)
  • Benkuan Cui, Kun Ma*, Leping Li, Weijuan Zhang, Ke Ji, Zhenxiang Chen, Ajith Abraham, "Intra-graph and Inter-graph Joint Information Propagation Network with Third-order Text Graph Tensor for Fake News Detection," Applied Intelligence, 2023, 53 (16): 18971–18988(IF: 5.019, CCF C类, Q2)
  • Kun Ma, Changhao Tang, Weijuan Zhang, Benkuan Cui, Ke Ji, Zhenxiang Chen, Ajith Abraham, "DC-CNN: Dual-channel Convolutional Neural Networks with Attention-pooling for Fake News Detection," Applied Intelligence, 2023, 53 (7): 8354–8369 (WOS: 000834007100001, IF: 5.019, CCF C类, Q2)
  • Changhao Tang, Kun Ma, Benkuan Cui, Ke Ji, Ajith Abraham, "Long Text Feature Extraction Network with Data Augmentation," Applied Intelligence, 2022, 52 (12): 17652–17667 (IF: 5.019, CCF C类, Q2)
  • Zhihao Hou, Kun Ma*, Yufeng Wang, Jia Yu, Ke Ji, Zhenxiang Chen, and Ajith Abraham, "Attention-based learning of self-media data for marketing intention detection," Engineering Applications of Artificial Intelligence, 2021, 98 (2): 104118: 1-9 (IF: 7.802, CCF C类, Q1)
  • Yufeng Wang, Kun Ma*, Laura Garcia-Hernandez, Jing Chen, Zhihao Hou, Ke Ji, Zhenxiang Chen, Ajith Abraham, "A CLSTM-TMN for Marketing Intention Detection," Engineering Applications of Artificial Intelligence, 2020, 91 (5): 1-9 (IF: 6.212, CCF C类, Q1)
  • Ziqiang Yu, Ajith Abraham, Xiaohui Yu, Yang Liu, Kun Ma*, Jing Zhou, "Improving the effectiveness of keyword search in databases using query logs," Engineering Applications of Artificial Intelligence, 2019, 81 (5): 169-179 (IF: 4.201, CCF C类, Q1)
  • Kun Ma, Bo Yang, Zhe Yang, and Ziqiang Yu, "Segment Access-aware Dynamic Semantic Cache in Cloud Computing Environment," Journal of Parallel and Distributed Computing, 2017, 110 (12): 42-51(IF: 1.815, CCF B类)
  • Ma, K. and Yang, B., "Large-scale Schema-free Data Deduplication Approach with Adaptive Sliding Window using MapReduce," The Computer Journal, 2015, 58 (11): 3187-3201 (IF:1, CCF B类)
  • Jing Chen, Kun Ma, Ke Ji, Zhenxiang Chen, "TM-HOL: Topic Memory model for Detection of Hate Speech and Offensive Language," Concurrency and Computation: Practice and Experience, 2021, Online (): e6754 (WOS: , IF: 1.536, EI: , CCF C类)
  • Kun Ma, Ziqiang Yu, Ke Ji, and Bo Yang, "Stream-Based Live Public Opinion Monitoring Approach with Adaptive Probabilistic Topic Model," Soft Computing, 2019, 23 (16): 7451-7470(IF: 2.04, CCF C类, Q3)
  • Kun Ma, Bo Yang, and Ziqiang Yu, "Optimization of Stream-based Live Data Migration Strategy in the Cloud," Concurrency and Computation: Practice and Experience, 2018, 30 (12): e4293: 1-18 (IF: 1.114, CCF C类)
  • Ma, K. and Yang, B., "Stream-based Live Data Replication Approach of In-memory Cache," Concurrency and Computation: Practice and Experience, 2017, 29 (11): e4052: 1-9 (IF: 1.133, CCF C类)
  • Ziqiang Yu, Fatos Xhafa, Yuehui Chen, Kun Ma, "A Distributed Hybrid Index for Processing Continuous Range Queries over Moving Objects," Soft Computing, 2019, 23 (9): 3191–3205 (IF: 2.367, CCF C类, Q3)
  • Ziqiang Yu, Yuehui Chen, Kun Ma*, "Real-time processing of k-NN queries over moving objects," Soft Computing, 2017, 21 (18): 5181-5191 (IF: 2.472, CCF C类)
  • Ke Ji, Zhenxiang Chen, Runyuan Sun, Kun Ma, Zhongjie Yuan, Guandong Xu, "GIST: A Generative Model with Individual and Subgroup-based Topics For Group Recommendation," Expert Systems with Applications, 2018, 94: 81-93 (IF: 3.928, CCF C类, Q2)
    • Proceedings
  • Yue Lu, Kun Ma and Jidong Duan, Influence Model of Paper Citation Networks with Integrated Weighted PageRank and HITS, Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2021), Dalian, May 5-7, 2021, 1081-1086 (CCF C)
  • Xiaoqian Zhang and Kun Ma*, Toward Sliding Time Window of Low Watermark to Detect Delayed Stream Arrival, Proceedings of the 2020 16th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020), Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 350), Shanghai, China, Oct. 16-18, 2020, 444-454 (CCF C)
  • Kun Ma, Kun Liu, and Lixin Du, Automated Assessment and Evaluation of Contribution of Collaborative Software Engineering Development Process, Proceedings of the 2020 27th Asia-Pacific Software Engineering Conference (APSEC 2020), Singapore, Singapore, Dec. 1-4, 2020, 500-504 (CCF C)
  • Fanghan Liu, Wenzheng Cai, and Kun Ma*, PLRS: Personalized literature hybrid recommendation system with paper influence, Proceedings of the 2020 20th International Conference, ICA3PP 2020, New York City, NY, USA, October 2–4, 2020, Proceedings, Part III, Lecture Notes in Computer Science 12454, New York, USA, Oct. 2-4, 2020, 703-705 (CCF C)
  • Yinnan Yao, Nan Su, Kun Ma*, UJNLP at SemEval-2020 Task 12: Detecting Offensive Language Using Bidirectional Transformers, Proceedings of the 2020 14th International Workshop on Semantic Evaluation (SemEval 2020), Barcelona, Spain, Dec. 12-13, 2020, 2203–2208 (Importance of papers in SemEval conference is just below 3 top NLP conferences (ACL/EMNLP/NAACL) in the field of Computational Linguistics.)
  • Yinnan Yao, Xiunan Zheng, and Kun Ma*, ILFS: Intelligent Lost and Found System using Multidimensional Matching Model, Proceedings of 2019 IEEE International Conference on Ubiquitous Intelligence & Computing (UIC 2019), Leicester, UK, August 19-23, 2019, 1205-1208 (CCF C类)
  • Kun Ma, Xuewei Niu, Ziqiang Yu, Ke Ji, POMon: From Simple Keyword Matching to Stream-based Live Probabilistic Topic Matching, Proceedings of 2018 IEEE Ubiquitous Intelligence & Computing (UIC 2018), Guangzhou, China, Oct. 8-12, 2018, 1189-1192 (CCF C类)
  • Xuewei Niu, Kun Ma*, Toward An Efficient Cache Management Framework, Proceedings of 2018 IEEE Ubiquitous Intelligence & Computing (UIC 2018), Guangzhou, China, Oct. 8-12, 2018, 1491-1496 (CCF C类)
  • Kun Ma, Ziqiang Yu, Ke Ji, Bo Yang, Stream-based Live Probabilistic Topic Computing and Matching, Proceedings of the 17th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2017), Part II, Lecture Notes in Computer Science, 10393, Helsinki, Finland, Aug. 21-23, 2017, 397-406 (CCF C类)
  • Kun Ma, Shuhui Liu, Yongzheng Lin, Ziqiang Yu, Ke Ji, Parallel Grouping Particle Swarm Optimization with Stream Processing Paradigm, Proceedings of 2017 IEEE 19th International Conference on High Performance Computing and Communications Workshops (HPCCWS 17) in conjunction with the 19th IEEE International Conference on High Performance Computing And Communications (HPCC 17), Bangkok, Thailand, Dec. 18-20, 2017, 22-26 (CCF C类)
  • Kun Ma, Xuewei Niu, Ziqiang Yu, Ke Ji, POMon: From Simple Keyword Matching to Stream-based Live Probabilistic Topic Matching, Proceedings of 2018 IEEE Ubiquitous Intelligence & Computing (UIC 2018), Guangzhou, China, Oct. 8-12, 2018, 1189-1192 (CCF C类)
  • Ma, K., Tang, Z., Zhong, J., Yang, B., LPSMon: A Stream-based Live Public Sentiment Monitoring System, Proceedings of The 17th International Conference on Web-Age Information Management, Part II, Lecture Notes in Computer Science , Nanchang, China, June 3-5, 2016, 9659: 534-536 (CCF C类)
  • Ma, K., Yang, B., Access-aware In-memory Data Cache Middleware for Relational Databases, Proceedings of 17th IEEE International Conference on High Performance Computing and Communications (HPCC 15), New York, USA, August 24 - 26, 2015, 1506-1511 (CCF C类)
  • Ma, K., Yang, B., and Chen, G., DOI Proxy Framework for Automated Entering and Validation of Scientific Papers, Proceedings of the 14th International Conference on Web-Age Information Management (WAIM 2013), Lecture Notes in Computer Science, Qinhuangdao, China, June 14-16, 2013, 2013, 7923: 799-801 (CCF C类)

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                Patent for InventionTOP

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                      Journal PapersTOP

                      2025

                        2024

                        • Jiaqi Fang, Kun Ma*, Yanfang Qiu, Ke Ji, Zhenxiang Chen, Bo Yang, "SEN-CTD: Semantic Enhancement Network with ContentTitle Discrepancy for Fake News Detection," International Journal of Web Information Systems, 2024, 20 (): 1-21
                          ISSN: , Date:
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                        • 纪科，张秀，马坤，孙润元，陈贞翔，邬俊, "基于关键实体和文本摘要多特征融合的话题匹配算法," 郑州大学学报(工学版), 2024, 45 (2): 51-59 
                          ISSN: , Date: 
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                        • Kun Ma, Qiang Wei, Weijuan Zhang, Yue Lu, and Ajith Abraham , "Ranking academic influence with integration of weighted PageRank and HITS for paper citation network," International Journal of Grid and Utility Computing, 2024, 132 (): 1-20 
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                        • Xinyu Liu, Kun Ma*, Ke Ji, Zhenxiang Chen, and Bo Yang, "Graph-based Multi-information Integration Network with External News Environment Perception for Propaganda Detection," International Journal of Web Information Systems, 2024, 20 (2): 195-212 (EI: )
                          ISSN: , Date: 2024/02/14
                             Abstract  BiBTeX
                           
                          Abstract
                          Purpose
                          Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.
                          Design/methodology/approach
                          G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.
                          Findings
                          G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.
                          Originality/value
                          An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.
                          BiBTeX
                        • Xinyu Liu, Kun Ma*, Qiang Wei, Ke Ji, Bo Yang, and Ajith Abraham, "G-HFIN: Graph-based Hierarchical Feature Integration Network for Propaganda Detection of We-media News Articles," Engineering Applications of Artificial Intelligence, 2024, 132 (): 1-16 (EI: 20240515464076, WOS: IF: 7.802, CCF-C, Q2, Top期刊)
                          ISSN: , Date: 2024/1/23
                          SCImago Journal & Country Rank
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                        • Qiang Wei, Kun Ma*, Xinyu Liu, Ke Ji, Bo Yang, and Ajith Abraham, "DIMN: Dual Integrated Matching Network for Multi-Choice Reading Comprehension," Engineering Applications of Artificial Intelligence, 2024, 130 (): 1-11 (EI: 20240115321602, WOS: IF: 7.802, CCF-C, Q2, Top期刊)
                          ISSN: 0952-1976, Date: 2023/12/28
                          SCImago Journal & Country Rank
                          Code and data   Abstract  BiBTeX
                           
                          Abstract
                          Multi-choice reading comprehension is a task that involves selecting the correct option from a set of option choices. Recently, the attention mechanism has been widely used to acquire embedding representations. However, there are two significant challenges: 1) generating the contextualized representations, namely, drawing associated information, and 2) capturing the global interactive relationship, namely, drawing local semantics. To address these issues, we have proposed the Dual Integrated Matching Network (DIMN) for multi-choice reading comprehension. It consists of two major parts. Fusing Information from Passage and Question-option pair into Enhanced Embedding Representation (FEER) is proposed to draw associated information to enhance embedding representation, which incorporates the information that reflects the most salient supporting entities to answer the question into the contextualized representations; Linear Integration of Co-Attention and Convolution (LIAC) is proposed to capture the interactive information and local semantics to construct global interactive relationship, which incorporates local semantics of a single sequence into the question-option-aware passage and passage-aware question-option representation. The experiments are shown that our DIMN performs better accuracy on three datasets: RACE (69.34%), DREAM (68.45%) and MCTest (71.81% on MCTest160 and 78.83% on MCTest500). Our DIMN is beneficial for improving the ability of machines to understand natural language. The system we have developed has been applied to customer service support. Our source code is accessible at https://github.com/vqiangv/DIMN}{https://github.com/vqiangv/DIMN.
                          BiBTeX
                          @article{WEI2024107694,
title = {DIMN: Dual Integrated Matching Network for multi-choice reading comprehension},
journal = {Engineering Applications of Artificial Intelligence},
volume = {130},
pages = {107694},
year = {2024},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2023.107694},
url = {https://www.sciencedirect.com/science/article/pii/S095219762301878X},
author = {Qiang Wei and Kun Ma and Xinyu Liu and Ke Ji and Bo Yang and Ajith Abraham},
keywords = {Multi-choice reading comprehension, Contextualized representation, Global interactive relationship, Attention, Convolution},
abstract = {Multi-choice reading comprehension is a task that involves selecting the correct option from a set of option choices. Recently, the attention mechanism has been widely used to acquire embedding representations. However, there are two significant challenges: (1) generating the contextualized representations, namely, drawing associated information, and (2) capturing the global interactive relationship, namely, drawing local semantics. To address these issues, we have proposed the Dual Integrated Matching Network (DIMN) for multi-choice reading comprehension. It consists of two major parts. Fusing Information from Passage and Question-option pair into Enhanced Embedding Representation (FEER) is proposed to draw associated information to enhance embedding representation, which incorporates the information that reflects the most salient supporting entities to answer the question into the contextualized representations; Linear Integration of Co-Attention and Convolution (LIAC) is proposed to capture the interactive information and local semantics to construct global interactive relationship, which incorporates local semantics of a single sequence into the question-option-aware passage and passage-aware question-option representation. The experiments are shown that our DIMN performs better accuracy on three datasets: RACE (69.34%), DREAM (68.45%) and MCTest (71.81% on MCTest160 and 78.83% on MCTest500). Our DIMN is beneficial for improving the ability of machines to understand natural language. The system we have developed has been applied to customer service support. Our source code is accessible at https://github.com/vqiangv/DIMN.}
}
                        • 马坤，邵永伟，郑楠，陈贞翔，杨波, "基于注意力机制的双向长短期记忆网络的在线工程实践评价框架," 软件导刊, 2024, 23 (8): 281-286 (CCF-T3, 中文核心期刊要目总览（扩展版）)
                          ISSN: , Date: 2024/08/15
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                        • 马坤，刘筱云，李乐平，纪科，陈贞翔，杨波, "用于意图识别的自适应多标签信息学习模型," 山东大学学报（工学版）, 2024, 54 (1): 45-51 (中国科学引文数据库来源期刊列表（CSCD）核心库，北大中文核心期刊)
                          ISSN: 1672-3961, Date: 
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                        2022
                        
							
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                        • 吕晓琦, 纪科, 陈贞翔, 孙润元, 马坤, 邬俊, 李浥东, "结合注意力与循环神经网络的专家推荐算法," 计算机科学与探索, 2021, 16 (9): 2068-2077 (CCF B类)
                          ISSN: 1673-9418, Date: 2021/03/26
                             Abstract  BiBTeX
                           
                          Abstract
                          在线问答社区（Community Question Answering， CQA）已经成为互联网最重要的知识分享交流平台，将用户提出的海量问题有效推荐给可能解答的用户，挖掘用户感兴趣的问题是此类平台最核心功能。一些针对问答社区的专家推荐算法已经被提出用来提高平台解答效率，但是现有工作大多关注于用户兴趣与问题信息匹配，忽视了用户兴趣动态变化问题，可能会严重影响推荐质量。本文提出了结合注意力与循环神经网络的专家推荐算法，不仅实现了问题信息的深度特征编码，而且还能捕获动态变化的用户兴趣。首先，问题编码器在预训练词嵌入基础上结合CNN卷积神经网络和Attention注意力机制实现了问题标题与绑定标签的深度特征联合表示。然后，用户编码器在用户历史回答问题的时间序列上利用长短期记忆神经网络Bi-GRU模型捕捉动态兴趣，并结合用户固定标签信息表征长期兴趣。最后，根据两个编码器输出向量的相似性计算产生用户动态兴趣与长期兴趣相结合的推荐结果。我们在来自于知乎问答社区的真实数据上进行了不同参数配置及不同算法的对比实验，表明该算法性能要明显优于目前比较流行的深度学习专家推荐算法。 
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                        • Yongzheng Lin, Hong Liu, Zhenxiang Chen, Kun Ma, "Machine learning-based classification of academic performance via imaging sensors," IEEE Sensors Journal, 2021, 21 (22): 24952-24958 (EI: 20205209676413, WOS: 000717802500010, IF: 4.325, CCF-C, Q2)
                          ISSN: 1530-437X, Date: 2021/11/15
                          SCImago Journal & Country Rank
                             Abstract  BiBTeX
                           
                          Abstract
                          Teaching is the primary component of the educational process for college students. To achieve the training objectives of different subjects, every University has arranged various types of courses. The ultimate purpose of teaching activities for these courses consists of improving the students’ knowledge level, ability level, and quality level. However, most of the current teaching activities only focus on the evaluation system and performance evaluation method for each course, thus lacking an overall academic performance and evaluation pipeline designed for the influence of different courses on students’ future development. Bearing the above-mentioned analysis in mind, we introduced machine learning-based techniques into students’ academic performance analysis and leveraged the learning-based approaches of rank models to establish a framework for students learning ability analysis. By employing imaging sensors, the capability of multiple media including image and video has also been embedded into the proposed architecture. Meanwhile, we exploited the pipeline for different students academic performance to reveal the academic commonality among students and the different influence of the primary courses for students’ future development between different groups.
                          BiBTeX
                          @ARTICLE{9286592,
  author={Y. {Lin} and H. {Liu} and Z. {Chen} and K. {Ma}},
  journal={IEEE Sensors Journal}, 
  title={Machine learning-based classification of academic performance via imaging sensors}, 
  year={2020},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/JSEN.2020.3043189}}
                        • Zhihao Hou, Kun Ma*, Yufeng Wang, Jia Yu, Ke Ji, Zhenxiang Chen, and Ajith Abraham, "Attention-based learning of self-media data for marketing intention detection," Engineering Applications of Artificial Intelligence, 2021, 98 (): 104118: 1-9 (EI: 20204909594046, WOS: 000606752400010, IF: 7.802, CCF-C, Q2, Top期刊)
                          ISSN: 0952-1976, Date: 2021/2/1
                          SCImago Journal & Country Rank
                          Code and data   Abstract  BiBTeX
                           
                          Abstract
                          In the context of natural language processing, accuracy of intention detection is the basis for subsequent research on human-machine speech interaction. However, the problem of ambiguity in word vectors reduces the accuracy of intent detection. Meantime, there is a disconnection between local features and global features as well, resulting in text feature extraction that cannot fully reflect semantic information. These issues are all barriers of intention detection. Therefore, this paper proposes an attention-based convolutional neural network for self-media data learning (called A-CNN) for marketing intention. We cascade the traditional CNN with the self-attention model in the Attention networks to form a new network structure called A-CNN, and put forward a fast feature extraction method based on skip-gram-based learning called FSLText, to represent the high-dimension word vectors in the A-CNN. On the premise of maintaining the advantages of the CNN, A-CNN can not only solve the problem of local and global features disconnection caused by the CNN pooling layer, but also avoid the increase of algorithm complexity. The Self-Attention mechanism in the Attention model can effectively optimize the weight of local features of the information in global features, and retain local features that are more useful for intention detection. A fast feature extraction method which is based on Skip-gram can retain the semantic and word order information of the text. The method is beneficial to the marketing intention detection. According to the experiment, our A-CNN, compared with traditional machine learning methods, can improve 12.32% accuracy. Contrast to the dual-channel CNN, the accuracy rate is improved by 9.68%, and compared with the ATT-CNN, it is improved by 9.97%. On the F1 score, the A-CNN can improve the F1 score by about 9.37% in comparison with the traditional machine learning methods, the accuracy rate is increased by 9.68% compared with the dual-channel CNN, and 9.6
                          BiBTeX
                          @article{HOU2021104118,
title = {Attention-based learning of self-media data for marketing intention detection},
journal = {Engineering Applications of Artificial Intelligence},
volume = {98},
pages = {104118},
year = {2021},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2020.104118},
url = {https://www.sciencedirect.com/science/article/pii/S0952197620303572},
author = {Zhihao Hou and Kun Ma and Yufeng Wang and Jia Yu and Ke Ji and Zhenxiang Chen and Ajith Abraham},
keywords = {Marketing intention detection, Attention model, Convolutional neural network, Feature extraction},
abstract = {In the context of natural language processing, accuracy of intention detection is the basis for subsequent research on human-machine speech interaction. However, the problem of ambiguity in word vectors reduces the accuracy of intent detection. Meantime, there is a disconnection between local features and global features as well, resulting in text feature extraction that cannot fully reflect semantic information. These issues are all barriers of intention detection. Therefore, this paper proposes an attention-based convolutional neural network for self-media data learning (called A-CNN) for marketing intention. We cascade the traditional CNN with the self-attention model in the Attention networks to form a new network structure called A-CNN, and put forward a fast feature extraction method based on skip-gram-based learning called FSLText, to represent the high-dimension word vectors in the A-CNN. On the premise of maintaining the advantages of the CNN, A-CNN can not only solve the problem of local and global features disconnection caused by the CNN pooling layer, but also avoid the increase of algorithm complexity. The Self-Attention mechanism in the Attention model can effectively optimize the weight of local features of the information in global features, and retain local features that are more useful for intention detection. A fast feature extraction method which is based on Skip-gram can retain the semantic and word order information of the text. The method is beneficial to the marketing intention detection. According to the experiment, our A-CNN, compared with traditional machine learning methods, can improve 12.32% accuracy. Contrast to the dual-channel CNN, the accuracy rate is improved by 9.68%, and compared with the ATT-CNN, it is improved by 9.97%. On the F1 score, the A-CNN can improve the F1 score by about 9.37% in comparison with the traditional machine learning methods, the accuracy rate is increased by 9.68% compared with the dual-channel CNN, and 9.68% in contrast with ATT-CNN. It illustrates that our A-CNN can effectively address semantic and feature selection for marketing intention detection.}
}
                        2020
                        
							
                        • 蔺永政,袁宁,赵亚欧,马坤,张坤,纪科, "程序设计类课程教学大纲的构建及优化," 中国成人教育, 2020, 29 (19): 52-54 
                          ISSN: 1004-6577, Date: 
                             Abstract  BiBTeX
                           
                          Abstract
                          根据计算机类工程教育专业认证的若干标准,结合地方经济的发展状况和济南大学计算机科学与技术专业的优势,比对工程教育专业认证所提出的各项毕业要求,以程序设计基础课程为例,结合自身实际进一步分解毕业要求指标点,并构建程序设计类课程目标对其形成支撑关系矩阵,制定课程大纲,充分体现"学生中心、产出导向、持续改进"的认证理念和教育导向,推动高校相关专业的认证,为培养适应地方经济发展和国际化需求的高级工程专业技术人才,从课程大纲的构建与优化方面提供参考。 
                          BiBTeX
                        • 蔺永政、赵秀阳、马坤、张坤、纪科、牛冬梅, "工程教育专业认证体系下《计算机图形学》教学大纲的构建及优化," 济南大学学报（社会科学版）, 2020, 30 (S1): 116-117 
                          ISSN: 1671-3842, Date: 
                             Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        • Yufeng Wang, Kun Ma*, Laura Garcia-Hernandez, Jing Chen, Zhihao Hou, Ke Ji, Zhenxiang Chen, Ajith Abraham, "A CLSTM-TMN for Marketing Intention Detection," Engineering Applications of Artificial Intelligence, 2020, 91 (): 103595: 1-9 (EI: 20201108304140, WOS: 000528195100023, IF: 6.212, CCF-C, Q2, Top期刊)
                          ISSN: 0952-1976, Date: 2020/5/1
                          SCImago Journal & Country Rank
                          Code and data   Abstract  BiBTeX
                           
                          Abstract
                          In recent years, neural network-based models such as machine learning and deep learning have achieved excellent results in text classification. On the research of marketing intention detection, classification measures are adopted to identify news with marketing intent. However, most of current news appears in the form of dialogs. There are some challenges to find potential relevance between news sentences to determine the latent semantics. In order to address this issue, this paper has proposed a CLSTM-based topic memory network (called CLSTM-TMN for short) for marketing intention detection. A ReLU-Neuro Topic Model (RNTM) is proposed. A hidden layer is constructed to efficiently capture the subject document representation, Potential variables are applied to enhance the granularity of subject model learning. We have changed the structure of current Neural Topic Model (NTM) to add CLSTM classifier. This method is a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN). The CLSTM structure has the ability to find relationships from a sequence of text input, and the ability to extract local and dense features through convolution operations. The effectiveness of the method for marketing intention detection is illustrated in the experiments. Our detection model has a more significant improvement in F1 (7%) than other compared models.
                          BiBTeX
                          @article{WANG2020103595,
title = "A CLSTM-TMN for marketing intention detection",
journal = "Engineering Applications of Artificial Intelligence",
volume = "91",
pages = "103595",
year = "2020",
issn = "0952-1976",
doi = "https://doi.org/10.1016/j.engappai.2020.103595",
url = "http://www.sciencedirect.com/science/article/pii/S0952197620300671",
author = "Yufeng Wang and Kun Ma and Laura Garcia-Hernandez and Jing Chen and Zhihao Hou and Ke Ji and Zhenxiang Chen and Ajith Abraham",
keywords = "Text classification, Marketing intention, Topic memory, News",
abstract = "In recent years, neural network-based models such as machine learning and deep learning have achieved excellent results in text classification. On the research of marketing intention detection, classification measures are adopted to identify news with marketing intent. However, most of current news appears in the form of dialogs. There are some challenges to find potential relevance between news sentences to determine the latent semantics. In order to address this issue, this paper has proposed a CLSTM-based topic memory network (called CLSTM-TMN for short) for marketing intention detection. A ReLU-Neuro Topic Model (RNTM) is proposed. A hidden layer is constructed to efficiently capture the subject document representation, Potential variables are applied to enhance the granularity of subject model learning. We have changed the structure of current Neural Topic Model (NTM) to add CLSTM classifier. This method is a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN). The CLSTM structure has the ability to find relationships from a sequence of text input, and the ability to extract local and dense features through convolution operations. The effectiveness of the method for marketing intention detection is illustrated in the experiments. Our detection model has a more significant improvement in F1 (7%) than other compared models."
}
                        • Zhe Yang, Kun Ma, Xiaoli Zhang, Lizhen Cui, Bo Yang, "RSCVC: Row-based Semantic Cache With Incremental Versioning Consistency," Concurrency and Computation: Practice and Experience, 2020, 32 (17): e5672:1-14 (WOS: 000563945600001, IF: 1.536, EI: 20201508392927, CCF-C)
                          ISSN: 1532-0634, Date: 2020/3/24
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          In the mobile computing environment, how to make the data access more efficient is a challenge due to the narrow communication bandwidth, the frequent disconnections of network, and the limited resources. Therefore, it is necessary to cache data on the client side. Besides, a good cache consistency method is essential to ensure the correctness. In this article, a row‐based semantic cache with incremental versioning consistency (RSCVC) is proposed. In RSCVC, we designed a semantic cache algorithm, a query trimming and optimizing algorithm, and a version‐based consistency strategy. This RSCVC cache mainly has two advantages. On one hand, it can obviously improve the response time of query and the hit ratio of the cache. On the other hand, the version‐based consistency enhances the stability of the system especially in high‐concurrency situations. Experiments demonstrate the efficacy of our proposed method and its superiority to state‐of‐the‐art methods.
                          BiBTeX
                          @article{doi:10.1002/cpe.5672,
author = {Yang, Zhe and Ma, Kun and Zhang, Xiaoli and Cui, Lizhen and Yang, Bo},
title = {RSCVC: Row-based semantic cache with incremental versioning consistency},
journal = {Concurrency and Computation: Practice and Experience},
volume = {n/a},
number = {n/a},
pages = {e5672},
keywords = {cache loading, cache penetration, cache snowslide, data consistency, query optimization, semantic cache},
doi = {10.1002/cpe.5672},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5672},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5672},
abstract = {Summary In the mobile computing environment, how to make the data access more efficient is a challenge due to the narrow communication bandwidth, the frequent disconnections of network, and the limited resources. Therefore, it is necessary to cache data on the client side. Besides, a good cache consistency method is essential to ensure the correctness. In this article, a row-based semantic cache with incremental versioning consistency (RSCVC) is proposed. In RSCVC, we designed a semantic cache algorithm, a query trimming and optimizing algorithm, and a version-based consistency strategy. This RSCVC cache mainly has two advantages. On one hand, it can obviously improve the response time of query and the hit ratio of the cache. On the other hand, the version-based consistency enhances the stability of the system especially in high-concurrency situations. Experiments demonstrate the efficacy of our proposed method and its superiority to state-of-the-art methods.}
}


                        • Min Yang, Kun Ma, and Xiaohui Yu, "An efficient index structure for distributed k-nearest neighbours query processing," Soft Computing, 2020, 24 (8): 5539–5550 (EI: 20184005885288, WOS: 000522353100005, IF: 3.05, CCF-C, Q3)
                          ISSN: 1432-7643, Date: 2018/9/26
                          SCImago Journal & Country Rank
                             Abstract  BiBTeX
                           
                          Abstract
                          Many location-based services are supported by the moving k-nearest neighbour (k-NN) query, which continuously returns the k-nearest data objects for a query point. Most of existing approaches to this problem have focused on a centralized setting, which show poor scalability to work around massive-scale and distributed data sets. In this paper, we propose an efficient distributed solution for k-NN query over moving objects to tackle the increasingly large scale of data. This approach includes a new grid-based index called Block Grid Index (BGI), and a distributed k-NN query algorithm based on BGI. There are three advantages of our approach: (1) BGI can be easily constructed and maintained in a distributed setting; (2) the algorithm is able to return the results set in only two iterations. (3) the efficiency of k-NN query is improved. The efficiency of our solution is verified by extensive experiments with millions of nodes.
                          BiBTeX
                        2019
                        
							
                        • 李浩、马坤、陈贞翔、赵川, "基于网络流量分析的未知恶意软件检测," 济南大学学报(自然科学版), 2019, 33 (6): 500-505 
                          ISSN: 1671-3559, Date: 2019/12/1
                             Abstract  BiBTeX
                           
                          Abstract
                          为了有效检测移动端的未知恶意软件,提出一种基于机器学习算法,并结合提取的具有鲁棒性的网络流量统计特征,训练出具有未知移动恶意网络流量识别能力的检测模型;该模型主要包括Android恶意软件样本数据预处理、网络流量数据自动采集以及机器学习检测模型训练;通过对不同时间节点的零日恶意软件检测的实验,验证模型的有效性。结果表明,所提出的方法对未知恶意样本的检测精度可以超过90%,并且F度量值为80%。
                          BiBTeX
                          @article{李浩2019基于网络流量分析的未知恶意软件检测,
  title={基于网络流量分析的未知恶意软件检测},
  author={李浩 and 马坤 and 陈贞翔 and 赵川},
  journal={济南大学学报(自然科学版)},
  number={6},
  year={2019},
}
                        • 段吉东，刘双荣，马坤，孙润元, "基于集成学习的情感分类方法," 济南大学学报(自然科学版), 2019, 33 (6): 483-488 
                          ISSN: 1671-3559, Date: 2019/12/1
                             Abstract  BiBTeX
                           
                          Abstract
                          针对自然语言处理的文本情感分类问题,提出一种基于集成学习的文本情感分类方法;基于微博数据的特殊性,首先对微博数据进行分词等预处理,结合词频-逆文档频率(TF-IDF)和奇异值分解(SVD)方法进行特征提取和降维,再通过堆叠泛化(stacking)集成学习的方式进行分类模型融合。结果表明,模型融合对文本情感分析的准确率达到93%,可以有效地判别微博文本的情感极性。
                          BiBTeX
                          @article{段吉东2019基于集成学习的文本情感分类方法,
  title={基于集成学习的文本情感分类方法},
  author={段吉东 and 刘双荣 and 马坤 and 孙润元},
  journal={济南大学学报(自然科学版)},
  year={2019},
}
                        • Yufeng Wang, Shuangrong Liu, Songqian Li, Jidong Duan, Zhihao Hou, Jia Yu, and Kun Ma*, "Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection," Future Internet, 2019, 11 (7): 155 (EI: 20193207277459; WOS: 000478637600017)
                          ISSN: 1999-5903, Date: 2019/7/10
                          SCImago Journal & Country Rank
                          Code and data   Abstract  BiBTeX
                           
                          Abstract
                          Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.
                          BiBTeX
                          @Article{fi11070155,
AUTHOR = {Wang, Yufeng and Liu, Shuangrong and Li, Songqian and Duan, Jidong and Hou, Zhihao and Yu, Jia and Ma, Kun},
TITLE = {Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection},
JOURNAL = {Future Internet},
VOLUME = {11},
YEAR = {2019},
NUMBER = {7},
ARTICLE-NUMBER = {155},
URL = {https://www.mdpi.com/1999-5903/11/7/155},
ISSN = {1999-5903},
ABSTRACT = {Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.},
DOI = {10.3390/fi11070155}
}




                        • Yongzheng Lin, Hong Liu, Zhenxiang Chen, Kun Zhang, Kun Ma, "Stream-based Data Sampling Mechanism for Process Object," CMC-Computers Materials & Continua, 2019, 60 (1): 245-257 (EI: 20193007229027; WOS: 000510451400016, IF: 4.89, Q3)
                          ISSN: 1546-2218, Date: 2019/6/26
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.
                          BiBTeX
                        • 马坤,蔺永政,韩士元,周劲,董吉文,杨波, "成果为导向多方共赢的校企协同创新创业实践方法与课程体系建设," 计算机教育, 2019, 21 (7): 102-106 (CCF-T2，本期封面文章，2019-2020全国计算机教育优秀论文一等奖【本期参评的1006篇论文仅评选出3个一等奖】)
                          ISSN: 1672-5913, Date: 2019/7/1
                             Abstract  BiBTeX
                           
                          Abstract
                          针对新工科背景下创新创业要求，分析国内建设情况，提出成果为导向多方共赢的校企协同创新创业实践方法，介绍具体措施，构建新工科背景下的校企协同创新创业课程体系，并说明创新模式和实施效果。
                          BiBTeX
                        • Ziqiang Yu, Ajith Abraham, Xiaohui Yu, Yang Liu, Jing Zhou, Kun Ma*, "Improving the effectiveness of keyword search in databases using query logs," Engineering Applications of Artificial Intelligence, 2019, 81 (): 169-179 (EI: 20191006592596, WOS: 000468721700014, IF: 4.201, CCF-C, Q2)
                          ISSN: 0952-1976, Date: 2019/5/1
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        • Kun Ma, Xuewei Niu, Ziqiang Yu, Ke Ji, "Toward an Aspect-oriented Cache Autoloading Framework with Annotation," International Journal of Web and Grid Services, 2019, 15 (3): 304-318 (EI: 20193007231472, WOS: 000476631000005, IF: 0.833)
                          ISSN: 1741-1106, Date: 2019/7/4
                          SCImago Journal & Country Rank
                             Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        • Kun Ma, Ziqiang Yu, Ke Ji, and Bo Yang, "Stream-Based Live Public Opinion Monitoring Approach with Adaptive Probabilistic Topic Model," Soft Computing, 2019, 23 (16): 7451-7470 (EI: 20182905568088, WOS: 000487983200001, IF: 3.05, CCF-C, Q3)
                          ISSN: 1432-7643, Date: 2018/7/17
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Public opinion monitoring, also known as first story detection, is defined within the topic detection and tracking on a particular Internet news event. Generally, it is used to find news propagation. Traditional method adopts text matching to address opinion monitoring. But it has some limitations such as hidden and latent topic discovery and incorrect relevance ranking of matching results on large-scale data. In this paper, we propose three solutions to live public opinion monitoring: simple keyword computing and matching, simple probabilistic topic computing and matching, and stream-based live probabilistic topic computing and matching. We point out the disadvantages of the first two solutions such as semantic matching and low efficiency on timely big data. Stream-based real-time topic computing and topic matching with query-time document and field boosting are proposed to make substantial improvements. Finally, our topic computing and matching experiments with crawled historical Netease news records show that our approaches are effective and efficient.
                          BiBTeX
                        • Ziqiang Yu, Fatos Xhafa, Yuehui Chen, Kun Ma, "A Distributed Hybrid Index for Processing Continuous Range Queries over Moving Objects," Soft Computing, 2019, 23 (9): 3191–3205 (EI: 20180104598030, WOS: 000465459700026, IF: 3.05, CCF-C, Q3)
                          ISSN: 1432-7643, Date: 2017/12/28
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        2018
                        
							
                        • Mostapha Zbakh, Kun Ma, "Recent advances in big data management-PART 1," Recent Patents on Computer Science, 2018, 11 (3): 160 (EI: 20190706513187)
                          ISSN: 2213-2759, Date: 2018/11/30
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        • 蔺永政,韩士元,孙雪松,周劲,马坤, "新工科背景下信息学科拔尖创新人才培养模式研究," 计算机教育, 2018, 20 (10): 73-76 (CCF-C)
                          ISSN: 1672-5913, Date: 2018/10/1
                             Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        • Kun Ma, Shuhui Liu, and Chunsun Duan, "A Column-aware Data Caching Method and System," Recent Patents on Computer Science, 2018, 11 (3): 161-168 (EI: 20190706514336)
                          ISSN: 2213-2759, Date: 2018/11/30
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Background: Large-scale data has brought more challenges in the aspects of efficient storage and access requirements. Due merely to differences in the programming interface and database schema, the emerging new database cannot replace RDBMS completely. Therefore, in a longer period in the future, schema-free databases that will assist RDBMS to address the access bottleneck is a broad solution of big data access in industry and academia.
                          BiBTeX
                        • Kun Ma, "Automatic Literature Metadata Extraction from DataCite Services," Recent Patents on Computer Science, 2018, 11 (1): 25-31 (EI: 20185006237148)
                          ISSN: 2213-2759, Date: 2018/10/18
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Background: Generally, it is difficult to obtain the literature metadata in a unified way because the source data of literature is heterogeneous. Researchers developed a series of systems marked by digital object to manage them and obtained a good effect. Though there are several DOI systems, we face with some problems in promoting the use of them.
                          BiBTeX
                           @article{Kun	  Ma* 2018
 Title = Automatic Literature Metadata Extraction from DataCite Services
 Journal = Recent Patents on Computer Science
 volume = 11
 Number = 1
 Pages = 25-31
 Year = 2018
 ISSN = 2213-2759/1874-4796
 DOI = 10.2174/2213275911666180627093515
 URL = http://www.eurekaselect.com/node/163243/article
 Author = Kun	  Ma*
 Keywords = Digital object identifier
 Keywords =   literature extraction
 Keywords =   metadata
 Keywords =   literature sharing
 Keywords =   bookmarklet
 Keywords =   proxy
 Keywords =   roadrunner
 Keywords =   template.
 Abstract = Background: Generally, it is difficult to obtain the literature metadata in a unified way
because the source data of literature is heterogeneous. Researchers developed a series of systems
marked by digital object to manage them and obtained a good effect. Though there are several DOI
systems, we face with some problems in promoting the use of them.
</P><P>
Objective: To address this issue of promoting literature identifier extraction, this paper has proposed
automatic literature metadata extraction from DataCite services.
</P><P>
Method: This paper describes Patent Publication Number CN103279361A, titled "Method and System
for Bookmark-triggered Literature Sharing", issued by State Intellectual Property Office of the
P.R.C. on January 27, 2016. A literature metadata extraction system supporting both personal computer
and mobile terminal is developed using the integration of DataCite content negotiation,
DataCite metadata search, and HTML template extraction. The architecture of this system is divided
into model, view, service and controller. An important contribution of this article is to design a
cross-platform and universal way to extract digital literature with/without DOI.
</P><P>
Results: The analysis of application's effect and piratical test case show the ability to verify the authenticity
of automatic literature metadata extraction from DataCite services. The contributions of
our method are literature identifier extraction from DOI proxy, template extraction using Roadrunner,
and bookmarklet-based literature sharing.
</P><P>
Conclusion: The idea and a disclosed embodiment of a patent (Patent CN103279361A, issued by
State Intellectual Property Office of the P.R.C.) are presented, which is based on the distribution of
literature metadata extraction. In one disclosed embodiment, this method contains literature identifier
extraction from DOI proxy, template extraction using Roadrunner, and bookmarklet-based literature
sharing.
 }
                        • Ke Ji, Zhenxiang Chen, Runyuan Sun, Kun Ma, Zhongjie Yuan, Guandong Xu, "GIST: A Generative Model with Individual and Subgroup-based Topics For Group Recommendation," Expert Systems with Applications, 2018, 94 (5): 81-93 (WOS: 000418218800008, IF: 5.452, EI: 20174404359833, CCF-C, Q2)
                          ISSN: 2666-8270, Date: 2018/3/1
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          In this paper, a Topic-based probabilistic model named GIST is proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members’ interest, but also consider some subgroups’ interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members’ choices and subgroups’ choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members’ interest and subgroups’ interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods.
                          BiBTeX
                          @article{JI201881,
title = "GIST: A generative model with individual and subgroup-based topics for group recommendation",
journal = "Expert Systems with Applications",
volume = "94",
pages = "81 - 93",
year = "2018",
issn = "0957-4174",
doi = "https://doi.org/10.1016/j.eswa.2017.10.037",
url = "http://www.sciencedirect.com/science/article/pii/S0957417417307200",
author = "Ke Ji and Zhenxiang Chen and Runyuan Sun and Kun Ma and Zhongjie Yuan and Guandong Xu",
keywords = "Group recommendation, Group activity, Decision making, Topic model, Recommender systems",
abstract = "In this paper, a Topic-based probabilistic model named GISTis proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members’ interest, but also consider some subgroups’ interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members’ choices and subgroups’ choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members’ interest and subgroups’ interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods."
}
                        • Kun Ma, Bo Yang, and Ziqiang Yu, "Optimization of Stream-based Live Data Migration Strategy in the Cloud," Concurrency and Computation: Practice and Experience, 2018, 30 (12): e4293: 1-18 (WOS: 000434082700003
, IF: 1.167, EI: , CCF-C)
                          ISSN: 1532-0634, Date: 2017/8/31
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Live data migration in the cloud is responsible to migrate blocks of data from one emigration node to several immigration nodes. However, live data migration strategy is a NP-hard problem like task scheduling. Recently, in-stream processing is a new technique to process large-scale data nearly instantaneously. This framework works fast that all decisions are made without a continuous stream of events. In this paper, we explore a real-time live data migration strategy with stream processing paradigm. First, the nonlinear migration cost model and balance model are introduced as the metrics to evaluate the data migration strategy. Subsequently, a live data migration strategy with particle swarm optimization (PSO) is proposed. Two improvement measures called loop context and particle grouping are proposed. As an improvement of stream processing framework, nested loop context structure is a feed back to support iterative optimization algorithm. As an improvement of PSO, grouping particles before in-stream processing is to speed up the convergence rate of PSO. Afterwards, we rebuild stream processing framework to implement these methods. The experimental results show the best performance of our method.
                          BiBTeX
                          @article{ma2017segment,
  title={Segment access-aware dynamic semantic cache in cloud computing environment},
  author={Ma, Kun and Yang, Bo and Yang, Zhe and Yu, Ziqiang},
  journal={Journal of Parallel and Distributed Computing},
  volume={110},
  pages={42--51},
  year={2017},
  publisher={Elsevier}
}
                        2017
                        
							
                        • Tianren Luo, Xueyong Li, Xiaoying Luo, Kun Ma*, "Toward Mobile Smart Data File Protection Box," International Journal of Autonomic Computing, 2017, 2 (3): 282-309 
                          ISSN: 1741-8569, Date: 2017/9/27
                             Abstract  BiBTeX
                           
                          Abstract
                          Aiming at this urgent need of the security protection of mobile intelligent terminal data file, we design a file security box to protect and manage important files in smartphones. The main functions of this file security box are: 1) fingerprint verification; 2) file management; 3) efficient encryption and decryption; 4) adaptive cipher algorithms; 5) separate logical document library; 6) updating the secret key regularly; 7) storing the key securely; 8) reinforcing the safety in enterprise level. Functional tests and performance tests show that the file security box we designed can not only achieve all of the above functionality with excellent user transparency and friendliness but also ensure the safety of important files without effecting users' experience.
                          BiBTeX
                          @article{luo2017toward,
  title={Toward mobile smart data file protection box},
  author={Luo, Tianren and Li, Xueyong and Ma, Kun and Luo, Xiaoying},
  journal={International Journal of Autonomic Computing},
  volume={2},
  number={3},
  pages={282--309},
  year={2017},
  publisher={Inderscience Publishers (IEL)}
}
                        • Kun Ma, Bo Yang, Zhe Yang, and Ziqiang Yu, "Segment Access-aware Dynamic Semantic Cache in Cloud Computing Environment," Journal of Parallel and Distributed Computing, 2017, 110 (12): 42-51 (WOS: 000411655400005, IF: 1.815, EI: 20172203704604, CCF-B, Q3)
                          ISSN: 0743-7315, Date: 2017/12/1
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          In recent years, researches focus on addressing the query bottleneck issue using semantic cache. However, the challenges of this method are how to increase cache hit ratio, decrease the query processing time, and address cache consistency issue. In this paper, we construct segment access-aware dynamic semantic cache for relational databases. Some definitions of semantic segment, probe query, and remainder query are proposed to describe the semantic cache. Then, estimation of the query result is proposed. Next, cache access algorithm of our proposed segment access-aware dynamic semantic cache is presented in case of cache exact hit, cache extended hit, cache partial hit and cache miss. Cache item with effective lifecycle tag is proposed to address cache consistency issue. Finally, experimental results show that this approach performs better than regular semantic cache and decisional semantic cache.
                          BiBTeX
                          @article{ma2017stream,
  title={Stream-based live data replication approach of in-memory cache},
  author={Ma, Kun and Yang, Bo},
  journal={Concurrency and Computation: Practice and Experience},
  volume={29},
  number={11},
  pages={e4052},
  year={2017},
  publisher={Wiley Online Library}
}
                        • Ma, K. and Yang, B., "Stream-based Live Data Replication Approach of In-memory Cache," Concurrency and Computation: Practice and Experience, 2017, 29 (11): e4052: 1-9 (WOS: 000400975800008, IF: 1.133, EI: 20170703347349, CCF-C)
                          ISSN: 1532-0634, Date: 2017/2/10
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Replication is a method to keep the consistency of source data and target data. In our previous work of access-aware in-memory data cache middleware for relational databases, the data are easy to be lost in case that power cuts off. Therefore, we investigate a live data replication approach from in-memory data cache to versioning repository in this paper. This method attempts to recover the in-memory data cache from the versioning repository in failure of access-aware in-memory data cache middleware. Although the replication is not a new problem, the state of art of the replication in the context of document stores is not mature. In our paper, we propose a live data replication approach of in-memory document stores using stream processing framework. First, we introduce cell state model to describe the replication process. To infinitely look back to any revision, we enable our proposed cell state model to support copy-modify-merge model to manage the changed data revisions subsequently. Finally, experimental results show that this approach is more suitable for the replication of continuous in-stream changed data compared with MapReduce-based batch replication.
                          BiBTeX
                          @article{ma2017stream,
  title={Stream-based live data replication approach of in-memory cache},
  author={Ma, Kun and Yang, Bo},
  journal={Concurrency and Computation: Practice and Experience},
  volume={29},
  number={11},
  year={2017},
  publisher={Wiley Online Library}
}
                        • Ziqiang Yu, Yuehui Chen, Kun Ma*, "Real-time processing of k-NN queries over moving objects," Soft Computing, 2017, 21 (18): 5181-5191 (EI: 20165003117640, WOS: 000410259700002, IF: 2.367, CCF-C, Q3)
                          ISSN: 1432-7643, Date: 2016/12/7
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Central to many location-based service applications is the task of processing k-nearest neighbor (k-NN) queries over moving objects. Many existing approaches adapt different index structures and design various search algorithms to deal with this problem. In these works, tree-based indexes and grid index are mainly utilized to maintain a large volume of moving objects and improve the performance of search algorithms. In fact, tree-based indexes and grid index have their own flaws for supporting processing k-NN queries over an ocean of moving objects. A tree-based index (such as R-tree) needs to constantly maintain the relationship between nodes with objects continuously moving, which usually causes a high maintenance cost. Grid index is widely used to support k-NN queries over moving objects, but the approaches based on grid index almost require an uncertain number of iterative calculations, which makes the performance of these approaches not predictable. To address this problem, we present a dynamic Strip Rectangle Index (SRI), which can reach a good balance between the maintenance cost and the performance of supporting k-NN queries over moving objects. SRI supplies two different index granularities that makes it better adapt to handle different data distributions than existing index structures. Based on SRI, we propose a search algorithm called SR-KNN that can rapidly calculate a final region with a filter-and-refine strategy to enhance the efficiency of process k-NN queries, rather than iteratively enlarging the search space like the grid-index-based approaches. Finally, we conduct experiments to fully evaluate the performance of our proposal.
                          BiBTeX
                          @article{yu2017real,
  title={Real-time processing of k-NN queries over moving objects},
  author={Yu, Ziqiang and Chen, Yuehui and Ma, Kun},
  journal={Soft Computing},
  pages={1--11},
  year={2017},
  publisher={Springer}
}
                        • Ma, K. and Yang, B., "Stream-based Live Entity Resolution Approach with Adaptive Duplicate Count Strategy," International Journal of Web and Grid Services, 2017, 13 (3): 351-373 (WOS: 000408254700007, EI: 20173003969041, IF: 1.105)
                          ISSN: 1741-1106, Date: 2017/7/3
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Recently, researchers have been more concerned about large-scale news and tweet data generated by the social media. Some cloud service providers utilise the data to find public sentiments for the tenants. The challenge is how to clean the big data in the cloud before making further analysis. To address this issue, we propose a new live entity resolution approach at a time to find duplicates from the news and tweet data. We investigate possible solutions to address live entity resolution in the cloud, to make sliding window size adaptive using multistep distance and window size dependent duplicate count strategy with alterable window step, and find duplicates by overlapping boundary objects in adjacent blocks. Finally, our experimental evaluation based on the news data on large datasets shows the high effectiveness and efficiency of the proposed approaches.
                          BiBTeX
                          @article{ma2017stream,
  title={Stream-based live entity resolution approach with adaptive duplicate count strategy},
  author={Ma, Kun and Yang, Bo},
  journal={International Journal of Web and Grid Services},
  volume={13},
  number={3},
  pages={351--373},
  year={2017},
  publisher={Inderscience Publishers (IEL)}
}
                        • Ma, K. and Yang, B., "Column Access-aware In-stream Data Cache with Stream Processing Framework," Journal of Signal Processing Systems, 2017, 86 (2): 191–205 (EI: 20161102079610, WOS: 000392386900007,IF: 0.893)
                          ISSN: 1939-8018, Date: 2016/3/5
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          In recent years, researches focus on addressing the query bottleneck issue of big data, e.g. NoSQL databases, MapReduce and big data processing framework. Although NoSQL databases have many advantages on On-Line Analytical Processing (OLAP), it is a big project to migrate Relational Database Management System (RDBMS) to NoSQL. Therefore, the optimization of RDBMS is still important. In this paper, we construct Column Access-aware In-stream Data Cache (CAIDC) for relational databases, which is an integral part of RDBMS and in-memory cache. Furthermore, a live synchronization approach from physical RDBMS to in-memory data cache using stream processing framework is proposed. On one hand, CAIDC provides low latency while supporting log-based trigger in the presence of updates to maintain data consistency because of stream processing framework. On the other hand, CAIDC translates the frequently accessed data to column-oriented in-memory cache by the column access frequency to ensure heavy h
                          BiBTeX
                          @article{ma2016column, title={Column Access-aware In-stream Data Cache with Stream Processing Framework},  author={Ma, Kun and Yang, Bo},  journal={Journal of Signal Processing Systems},  pages={1--15},  year={2016},  publisher={Springer}}
                        2016
                        
							
                        • Ma, K., Yang, B., and Abraham, A., "Asynchronous Data Translation Framework for Converting Relational Tables to Document Stores ," International Journal of Computers and Applications, 2016, 38 (1): 19-28 (EI: 20171203460071)
                          ISSN: 1206-212X, Date: 2016/1/2
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Although Not Only SQL (NoSQL) techniques have some new features such as query optimization and caching in the era of big data, it is not mature enough to replace traditional Relational Database Management System (RDBMS). In a long period of time, the hybrid solution of RDBMS and NoSQL is a main steam technology. In this paper, we present an asynchronous data framework to translate relational tables into schema-free document stores. The challenge of this framework is how to ensure that the translation is transparent and pervasive to the application with minimal impact on RDBMS performance. To better describe the translation process, we present the intermediate cell state model (CSM) to describe versioning incremental state. We address this challenges by translating the binary log of RDBMS to CSM repository, and the repository is reset to the NoSQL when encountering schema adjustment asynchronously. Finally, we leverage the CSM rectification algorithm and NoSQL synchronization algorithm to implement the whole framework. The experimental results illustrate that this translation has a less minimal impact on RDBMS performance and lower cost than other solutions.
                          BiBTeX
                          @article{doi:10.1080/1206212X.2016.1188563,author = {Ma,Kun AND Yang,Bo AND Abraham,Ajith},title = {Asynchronous DATA Translation Framework FOR Converting Relational TABLES TO Document Stores},journal = {International Journal of Computers AND Applications},YEAR = {2016},doi = {10.1080/1206212X.2016.1188563},}
                        • Ma, K., Teng, H., Du, L., Zhang, K., "Exploring Mobile Instant Messaging and Self-service to Leverage Learner Participation for Collaborative Teaching," International Journal of Continuing Engineering Education and Life-Long Learning, 2016, 26 (3): 330-334 (EI: 20163502753914)
                          ISSN: 1560-4624, Date: 2016/8/20
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          With the development of Web 2.0 techniques, more and more learning management systems are developed to assist teaching and learning in higher schools. However, current systems lack interactive interactions between instructors and pupils. To address this limitation, this paper explores mobile instant messaging and self-service to leverage learner participation for collaborative teaching. We design a mobile learning management system using WeChat platform APIs to archive this goal. The architecture, features and discussions are given to present the advantages of this system. Some survey results ranging from adoption, satisfaction and psychological comfort present the feasibility of our system.
                          BiBTeX
                          @article{ma2016exploring,
  title={Exploring mobile instant messaging and self-service to leverage learner participation for collaborative teaching},
  author={Ma, Kun and Teng, Hao and Du, Lixin and Zhang, Kun},
  journal={International Journal of Continuing Engineering Education and Life Long Learning},
  volume={26},
  number={3},
  pages={330--344},
  year={2016},
  publisher={Inderscience Publishers (IEL)}
}
                        • Kun Ma, "A Versioning-based Acceleration Method for Software Online Upgrade," Recent Patents on Computer Science, 2016, 9 (1): 81-88 (EI: 20161402178114)
                          ISSN: 2213-2759, Date: 2016/3/11
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          This paper describes Patent Publication Number CN102868731A, titled ”Method and appliance for software online upgrade and downloading acceleration”, issued by State Intellec- tual Property Office of the P.R.C. on January 9, 2013. The patent is based on versioning-based acceleration method for software online upgrade. First, client computer system requests distributed hash table (DHT) nodes to obtain update resources. Second, DHT node discovers resource seeds (version control server) or peers (client systems). Finally, resource seeds send the client a list of applicable updates. The patent’s applicability has been illustrated by efficiently solving software online upgrade problems.
                          BiBTeX
                          @article{ma2016versioning,
  title={A Versioning-based Acceleration Method for Software Online Upgrade},
  author={Ma, Kun},
  journal={Recent Patents on Computer Science},
  volume={9},
  number={1},
  pages={81--88},
  year={2016},
  publisher={Bentham Science Publishers}
}
                        • Kun Ma, Hao Teng, Lixin Du, Kun Zhang, "Exploring Model Driven Engineering Method for Teaching Software Engineering," International Journal of Continuing Engineering Education and Life-Long Learning, 2016, 26 (3): 294-308 (EI: 20163502753912)
                          ISSN: 1560-4624, Date: 2016/3/4
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Model-driven engineering (MDE) is an emerging software engineering discipline, which raised the level of abstraction. Although there are best practices in industry, the number of experiences describing how MDE is applied in software engineering teaching in higher school is still limited. In our previous work, students fail to implement a demonstrative system though they can hand in reports in time. In this article, we explore a model-driven engineering method for teaching software engineering. We present several factors affecting team project, and design both semi-model-driven project and code generation project in detail. Finally, we do an in-depth analysis on industrial context, size of project team, roles of university and company, and student satisfaction. Some lessons learned from the experiences of both projects are concluded as choice of pilot project, involvement of company and training before team project. The organisation of both projects is served as an inspiration for higher schools that want to develop a software engineering course.
                          BiBTeX
                          @article{ma2016exploring,
  title={Exploring mobile instant messaging and self-service to leverage learner participation for collaborative teaching},
  author={Ma, Kun and Teng, Hao and Du, Lixin and Zhang, Kun},
  journal={International Journal of Continuing Engineering Education and Life Long Learning},
  volume={26},
  number={3},
  pages={330--344},
  year={2016},
  publisher={Inderscience Publishers (IEL)}
}
                        2015
                        
							
                        • Ma, K. and Yang, B., "Asynchronous Adaptive Delay Tolerant Index Cache Using In-memory Delta Cell," Informatica, An International Journal of Computing and Informatics, 2015, 39 (4): 443-450 (EI: 20160301821359)
                          ISSN: 0350-5596, Date: 
                             Abstract  BiBTeX
                           
                          Abstract
                          Relational database indexes, used to speed up access to data stored in a database, are maintained when data in the source table of the index is modified. Therefore, relational database index management can involve time consuming manual analysis and specialized development efforts, and impose organizational overhead and database usage costs, especially in the context of big data. To address this limitation, this paper proposes an asynchronous adaptive delay tolerant index cache using in-memory delta cell. The contributions of index cache are adaptive management and fine-grained delta cell. Finally, our experimental evaluation shows that this simple index cache has the features such as update efficiency with frequent changes, transparency to developers, and low impact on database performance.
                          BiBTeX
                          @article{ma2015asynchronous,
  title={Asynchronous adaptive delay tolerant index cache using in-memory delta cell},
  author={Ma, Kun and Yang, Bo},
  journal={Informatica},
  volume={39},
  number={4},
  pages={443},
  year={2015},
  publisher={Slovenian Society Informatika/Slovensko drustvo Informatika}
}
                        • Yu, Z. and Ma, K., "Toward Core Point Evolution Using Water Ripple Model," WSEAS Transactions on Computers, 2015, 14 (Art. #79): 819-825 
                          ISSN: 1109-2750, Date: 
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          This article presents software library for the Arduino platform which significantly improves the speed of the functions for digital input and output. This allows the users to apply these functions in whole range of applications, without being forced to resort to direct register access or various 3rd party libraries when the standard Arduino functions are too slow for given application. The method used in this library is applicable also to other libraries which aim to abstract the access to general purpose pins of a microcontroller.
                          BiBTeX
                          @article {YuToward2015, title={Toward Core Point Evolution Using Water Ripple Model}, author={Zhibing Yu and Kun Ma}, journal={WSEAS Transactions on Computers}, pages={819-825}, year={2015}, volume={14}, number={Art. #79}}
                        • Kun Ma and Bo Yang, "Large-scale Schema-free Data Deduplication Approach with Adaptive Sliding Window using MapReduce," The Computer Journal, 2015, 58 (11): 3187-3201 (EI: 20154801604848, WOS:000365157000025, IF:1, CCF-B, Q3)
                          ISSN: 0010-4620, Date: 2015/7/18
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Data deduplication is the task of identifying all groups of objects within one or several data sets, respectively. However, this task will become difficult in the context of big data. To address this limitation, we propose a new schema-free data deduplication approach in parallel in the aspect of breeding data deduplication related to food safety. Although MapReduce framework enables efficient parallel execution of data-intensive tasks, it cannot find duplicates in adjacent block. Furthermore, current deduplication approaches with MapReduce are restricted to fixed sliding window. Therefore, we investigate possible solutions to improve current deduplication approaches with MapReduce, to make sliding window size adaptive using adaptive multiple duplicate count strategy with alterable window step, and find duplicates by overlapping boundary objects in adjacent blocks. Moreover, we propose a multi-pass Partition-Sort-Map-Reduce approach with adaptive sliding window to speed up the deduplication process. Finally, our experimental evaluation based on the breeding data on large datasets shows the high effectiveness and efficiency of the proposed approaches.
                          BiBTeX
                          @article {MaLarge2015, title={Large-scale Schema-free Data Deduplication Approach with Adaptive Sliding Window using MapReduce}, author={Kun Ma and Fusen Dong and Bo Yang}, journal={The Computer Journal}, pages={1-15}, year={2015}, volume={58}}
                        • Ma, K., Tang, Z., Yang, Z., Yao, S., "AtMe: An Online Multi-tenant Social Networking Service in Campus," International Journal of Database Theory and Application, 2015, 8 (5): 183-194 (EI: 20161002078171)
                          ISSN: 2005-4270, Date: 2015/10/31
                             Abstract  BiBTeX
                           
                          Abstract
                          Recently, more and more undergraduates have turned Internet to find support and take part in society in campus. However, current SNS systems are not oriented to a specific campus. In this paper, we design an online multi-tenant social networking service in campus, including At Helper and At Society. At Helper aims at improving the success ratio of the help process with a multi-tenant architecture, and At Society provides social services using popular instant messaging mobile application. Both modules change the traditional operation and maintenance to rent services. Besides, the key innovative design, such as multi-tenancy and access-aware data cache, are discussed in detail.
                          BiBTeX
                          @article{MaAtMe2015,  title={AtMe: An Online Multi-tenant Social Networking Service in Campus},  author={Kun Ma and Zijie Tang and Zhe Yang and Shuwei Yao},  journal={nternational Journal of Database Theory and Application},  volume={8}, number={5} , pages={183-194},  year={2015}}
                        • Kun Ma, Funsen Dong, "Live Data Migration Approach from Relational Tables to Schema-free Collections with MapReduce," International Journal of Services Technology and Management  , 2015, 21 (4/5/6): 318-335 (EI: 20160201785010)
                          ISSN: 1460-6720, Date: 2015/12/31
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Although NoSQL has some new features to address the query bottleneck of big data, the hybrid solution of relational database and NoSQL will last for a long period. Therefore, recent researches focus on the data migration issue from relational database to document stores. However, there are few publications on live data migration in parallel. In this paper, we attempt to address this old problem using new MapReduce framework. The process of migration consists of log-based change data capture, merging of changed data, and blocking and transformation. We utilize predicate logic of mathematical relation and QVT relations to describe the mapping rules clearly. Finally, our experimental evaluation of log-based blocking and transformation with MapReduce shows the higher effectiveness and efficiency than current methods.
                          BiBTeX
                          @article {MaLive2015, title={Live Data Migration Approach from Relational Tables to Schema-free Collections with MapReduce}, author={Kun Ma and Fusen Dong}, journal={International Journal of Services Technology and Management}, pages={1-15}, year={2015}, volume={21}, number={online}}
                        • Dong, F., Yang, B., Ma, K., and Wang, W., "Incremental duplicate data detection method with MapReduce," Journal of University of Jinan (Science and Technology), 2015, 29 (4): 241-245 
                          ISSN: 1671-3559, Date: 2015/8/1
                             Abstract  BiBTeX
                           
                          Abstract
                          针对重复数据检测过程中增量数据重复值检测问题进行分析,在基本近邻排序算法基础上,提出增量近邻排序比较算法。该算法通过跳动窗口形式比较相邻数据,大大减少了数据比较次数;同时引入MapReduce模型对该算法加以改进以提高其海量数据处理的能力。实验表明,改进后的增量近邻排序比较算法在保证检则结果准确的前提下,能够有效提高增量数据重复检测的速度,并且算法具有较高的稳定性,更适应海量数据环境中重复数据检测任务。
                          BiBTeX
                          @article{董富森2015mapreduce,  title={MapReduce 模型下增量重复数据检测方法},  author={董富森 and 杨波 and 马坤 and 王文华},  journal={济南大学学报 (自然科学版)},  volume={4},  pages={001},  year={2015}}
                        • Dong, F., Ma, K., and Yang, B., "Cache System for Frequently Updated Data in the Cloud," WSEAS Transactions on Computers, 2015, 14 (Art. #17): 163-170 
                          ISSN: 1109-2750, Date: 
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Maintaining data indexes and query cache becomes the bottleneck of the database, especially in the context of frequently updated data. In order to reduce the burden of the database, a cache system for frequently updated data has been proposed in this paper. In the system, update statements are parsed firstly. Then updated data are saved as key-value pairs in the cache and they are synchronized into the database at idle time. Experimental results show that the proposed cache system cannot only accelerate the data updating rate, but also improve the data writing ability in maintaining indexes and consistency of cache data greatly.
                          BiBTeX
                          @article {DongCache2015, title={Cache System for Frequently Updated Data in the Cloud}, author={Fusen Dong and Kun Ma and Bo Yang}, journal={WSEAS Transactions on Computers}, pages={163-170}, year={2015}, volume={14}, number={Art. #17}}
                        • Ma, K., and Zhang, W., "Introducing Browser-based High-Frequency Cloud Monitoring System using WebSocket Proxy," International Journal of Grid and Utility Computing, 2015, 6 (1): 21-29 (EI: 20145200372356)
                          ISSN: 1741-847X, Date: 2015
                          acceptance rate: 11/69=15.9%
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Currently, monitoring Cloud resources is becoming a priority. It is a very important link of building B/S monitoring systems to gather the data from the Clouds. However, one of the key challenges that holds Cloud monitoring is that, B/S monitoring systems lack the support of the high-frequency monitoring. To address this limitation, this paper aims to provide an high-frequency monitoring approach to integrate with Clouds. We design a WebSocket proxy as the high-speed and low-overhead communication interface between browsers and servers. Moreover, we introduce a browser-based high-frequency Cloud monitoring system (BHCMS) to enable a new monitoring of dynamic and responsive applications. The important finding of this paper is that monitoring data is updated in the browser at a rate of high frequency, without using any browser plug-ins. The proposed WebSocket proxy also provides WebSocket emulation for those browsers without native WebSocket capability transparently, to support a wide variety of popular legacy browsers. The experimental results show that the average latency time of our WebSocket monitoring system is generally lower than polling, FlashSocket and Socket solution. Finally, the article concludes with a brief discussion of possible future activities, which might be pursued to enable the other similar monitoring to benefit from this technology.
                          BiBTeX
                          @article {MaIntroducing2015, title={Introducing Browser-based High-Frequency Cloud Monitoring System using WebSocket Proxy}, author={Kun Ma and Weijuan Zhang}, journal={International Journal of Grid and Utility Computing}, pages={21-29}, year={2015}, volume={6}, number={1}}
                        • Ma, K., and Abraham, A., "Introducing Versioning-based Software Online Upgrade Framework over a Peer-to-Peer Network," Computing and Informatics, 2015, 34 (6): 1357-1373 (WOS: 000370996500007, IF: 0.524)
                          ISSN: 1335-9150, Date: 
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        • Ma, K., and Yang, B., "Introducing Extreme Data Storage Middleware of Schema-free Document Stores using MapReduce," International Journal of Ad Hoc and Ubiquitous Computing, 2015, 20 (4): 274–284 (WOS: 000366130900006, EI: 20155201714641, IF: 0.489)
                          ISSN: 1743-8225, Date: 2015
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Referred to NoSQL, schema-free databases feature elasticity and scalability in combination with a capability to store big data and work with Cloud computing systems, all of which make them extremely popular. In particular, the creation of the data warehouse is gaining a significant momentum. Although large corporate early adopters paved the way, since then, data warehousing has been embraced by organizations of all sizes. However, there are few publications on data warehouse of NoSQL. In this paper, an extreme data storage middleware (EDSM) of schema-free document stores using MapReduce is presented to address the issue of formulating no redundant data warehouse with small amount of storage space for the purpose of their composition in a way that utilizes the MapReduce framework. First, the definition of cell with an effective lifecycle tag is given. Second, the architecture and extreme data storage principles are presented. At last, the capture-map-reduce procedures are discussed to create the NoSQL data warehouse. The experiment is shown to successfully build the NoSQL data warehouse reducing data redundancy compared with document with timestamp and lifecycle tag solutions. Our experiment also provides insight into some of the key challenges and shortcomings that researchers and engineers face when designing the data warehouse middleware.
                          BiBTeX
                          @article {Ma2015Introducing, title={Introducing Extreme Data Storage Middleware of Schema-free Document Stores using MapReduce}, author={Kun Ma and Bo Yang}, journal={International Journal of Ad Hoc and Ubiquitous Computing}, pages={274–284}, year={2015}, volume={20}, number={4}}
                        • Ma, K., and Yang, B., "Experiences in Teaching Virtualization with Private Cloud Platform," International Journal of Continuing Engineering Education and Life-Long Learning, 2015, 25 (3): 301-314 (EI: 20154601545750)
                          ISSN: 1560-4624, Date: 2015/12/20
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        2014
                        
							
                        • Ma, K., Teng, H., Du, L., Zhang, K., "Project-Driven Learning-by-Doing Method for Teaching Software Engineering using Virtualization Technology," International Journal of Emerging Technologies in Learning, 2014, 9 (9): 26-31 (EI: 20144900275315)
                          ISSN: 1863-0383, Date: 2014/10/25
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Many universities are now offering software engineering an undergraduate level emphasizing knowledge point. However, some enterprise managers reflected that education ignore hands-on ability training, and claimed that there is the isolation between teaching and practice. This paper presents the design of a Software Engineering course (sixth semester in network engineering) at University of Jinan for undergraduate Software Engineering students that uses virtualization technology to teach them project-driven learning-by-doing software development process. We present our motivation, challenges encountered, pedagogical goals and approaches, findings (both positive experiences and negative lessons). Our motivation was to teach project-driven Software Engineering using virtualization technology. The course also aims to develop entrepreneurial skills needed for software engineering graduates to better prepare them for the software industry. Billing models of virtualization help pupils and instructors find the cost of the experiment. In pay-as-you-go manner, two labs and three step-by-step projects (single project, pair project, and team project) are designed to help the students to complete the assignment excitedly. We conduct some detailed surveys and present the results of student responses. The assessment process designed for this course is illustrated. The paper also shows that learning-by-doing method correlates with the characteristics of different projects, which has resulted in a successful experience as reported by students in an end of a semester survey.
                          BiBTeX
                          @article {MaProject2014, title={Project-Driven Learning-by-Doing Method for Teaching Software Engineering using Virtualization Technology}, author={Kun Ma, Hao Teng, Lixin Du, and Kun Zhang}, journal={International Journal of Emerging Technologies in Learning}, pages={26-31}, year={2014}, volume={9}, number={9}}
                        • Ma, K., and Yang, B., "Multiple wide tables with vertical scalability in multitenant sensor cloud systems," International Journal of Distributed Sensor Networks, 2014, 2014 (): 10 pages (EI: 20141917689293, WOS: 000334228200001, IF: 0.923, Q3)
                          ISSN: 1550-1329, Date: 2014/4/1
                          SCImago Journal & Country Rank
                          acceptance rate: 37% 
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Software-as-a-service (SaaS) has emerged as a new computing paradigm to provide reliable software on demand. With such an inspiring motivation, sensor cloud system can benefit from this infrastructure. Generally, sharing database and schema is the most commonly used data storage model in the cloud. However, the data storage of tenants in the cloud is approaching schema null and evolution issues. To address these limitations, this paper proposes multi-tenant multiple wide tables with vertical scalability by analyzing the features of multi-tenant data. To solve schema null issue, extended vertical part is used to trim down the amount of schema null values. To reduce probability of schema evolution, wide table is divided into multiple clusters that we called multiple wide tables. This design reaches the balance between tenant customizing and its performance. Besides, the partition and correctness of multiple wide tables with vertical scalability are discussed in detail. The experimental results indicate that the solution of our multiple wide tables with vertical scalability is superior to single wide table, and single wide table with vertical scalability in the aspects of spatial intensity and read performance.
                          BiBTeX
                          @article {MaMultiple2014, title={Multiple Wide Tables with Vertical Scalability in Multi-tenant Sensor Cloud Systems}, author={Kun Ma and Bo Yang}, journal={International Journal of Distributed Sensor Networks}, pages={10 pages}, year={2014}, volume={2014}, number={Article ID 583686}}
                        • Ma, K., Zhang, W., and Tang, Z., "Toward Fine-grained Data-level Access Control Model for Multi-tenant Applications," International Journal of Grid and Distributed Computing, 2014, 7 (2): 79-88 (EI: 20142017707412)
                          ISSN: 2005-4262, Date: 2014/4/30
                            Abstract  BiBTeX   Abstract  BiBTeX
                           
                          Abstract
                          Cloud computing presents new security and privacy challenges to control access to multi-tenant applications in the cloud. However, this solution has more challenges once the number of access control list (ACL) increases in the cloud, such as efficiency of policy resolution, multi-tenancy and data isolation. To address these limitations, this paper describes a fine-grained data-level access control model (FDACM) suitable for multi-tenant applications where role-based and data-based access control are both supported. Lightweight expressions are proposed to present complicated policy rules in our solution. Moreover, we discuss the most important part of FDACM in detail: query privilege model and decision privilege model. Furthermore, we also propose the architecture and authorization procedure which implements these two models. Some technical implementation details together with the performance results from the prototype are provided. Finally, a case study of FDACM is illustrated to evaluate the effect of the application in practice.
                          BiBTeX
                          @article {MaToward2014, title={Toward Fine-grained Data-level Access Control Model for Multi-tenant Applications}, author={Kun Ma and Weijuan Zhang and Zijie Tang}, journal={International Journal of Grid and Distributed Computing}, pages={79-88}, year={2014}, volume={7}, number={2}}
                        • Ma, K., and Zhang, L., "Bookmarklet-triggered Unified Literature Sharing System in the Cloud," International Journal of Grid and Utility Computing, 2014, 5 (4): 217-226 (EI: 20144400146172)
                          ISSN: 1741-847X, Date: 2014
                          special issue acceptance rate: 5/27=18.5%
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Currently, modern researchers need convenient approaches and tools for sharing the literature what is available to them. For the purpose of simplifying the literature sharing and elaborating the dynamic role of the human being, we introduce a bookmarklet-triggered unified literature sharing system (BULSS) in the Cloud. After clicking the bookmarklet, the sidebar is displayed on the web. In this system, the related literature is pushed to the researchers automatically. The methodology to design the sidebar client, Cloud process engine and bookmarklet is presented. Besides, the architectural details of the components are described. This system allows easy manipulation of the literature sharing and academic exchange, which are used frequently and are very often necessary in scientific activity such as research, writing papers and dissertations, and preparing reports.
                          BiBTeX
                          @article {MaBookmarklet2014, title={Bookmarklet-triggered Unified Literature Sharing System in the Cloud}, author={Kun Ma and Lei Zhang}, journal={International Journal of Computers and Applications}, pages={217-226}, year={2014}, volume={5}, number={4}}
                        • Ma, K., and Tang, Z., "An Online Social Mutual Help Architecture for Multitenant Mobile Clouds," International Journal of Intelligent Information and Database Systems, 2014, 8 (4): 359-374 (EI: 20151500735359)
                          ISSN: 1751-5858, Date: 2014
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Social mutual help is generally defined as an exchange of verbal and non-verbal messages which convey a plea for help. Recently, more and more people have turned Internet to find support and share their experiences. However, the success rate of this online social mutual help is relatively low. Besides, clients are forced to purchase a software to provide services to their users with high cost. Therefore, we aim at improving the success ratio of the help process with a multi-tenant architecture. In this paper, we design an online social mutual help architecture for multi-tenant mobile Clouds. First, we propose a helper recommendation algorithm to accelerate the help process. Second, we present the integration schema of push and pull to propagate the feeds to the potential helpers. Third, we provide some RESTful Web service APIs for the integration with third-party systems. The experimental results show that this architecture improves the success rate and performance successfully and provides the support of data customizing of tenants to increase the sparsity.
                          BiBTeX
                          @article{ma2015online,  title={An Online Social Mutual Help Architecture for Multitenant Mobile Clouds},  author={Ma, K and Tang, Z},  journal={International Journal of Intelligent Information and Database Systems, online},  year={2015}}
                        • Tang, Z. and Ma, K., "RSSCube: A Content Syndication and Recommendation Architecture," International Journal of Database Theory and Application, 2014, 7 (4): 237-248 (EI: 20143718151244)
                          ISSN: 2005-4270, Date: 2014/8/31
                             Abstract  BiBTeX
                           
                          Abstract
                          Content syndication is the process of pushing the information out into third-party information providers. The idea is to drive more engagement with your content by wiring it into related digital contexts. However, there are some shortages of current related products, such as search challenges on massive feeds, synchronization performance, and user experience. To address these limitations, we aim to propose an improved architecture of content syndication and recommendation. First, we design a source listener to extract feed changes from different RSS sources, and propagate the incremental changes to target schema-free document stores to improve the search performance. Second, the proposed recommendation algorithm is to tidy, filter, and sort all the feeds before pushing them to the users automatically. Third, we provide some OAuth2-authorization RESTful feed sharing APIs for the integration with the third-party systems. The experimental result shows that this architecture speeds up the search and synchronization process, and provides friendlier user experience.
                          BiBTeX
                          @article {TangRSSCube2014, title={RSSCube: A Content Syndication and Recommendation Architecture}, author={Zijie Tang and Kun Ma}, journal={International Journal of Database Theory and Application}, pages={237-248}, year={2014}, volume={7}, number={4}}
                        • Ma, K., Dong, F. and Yang, B., "Incremental Object Matching Approach of Schema-free Data with MapReduce," International Journal of Computers and Applications, 2014, 36 (2): 72-77 (EI: 20143718148180)
                          ISSN: 1206-212X, Date: 2014
                          SCImago Journal & Country Rank   Abstract  BiBTeX
                           
                          Abstract
                          Object matching is a traditional deduplication approach, that is used to identify the duplicates within one or several data sets respectively. However, this task will become difficult in the context of big data. Few publications have mentioned incremental methods to solve this legacy issue in parallel. To address this limitation, we aim to propose an incremental object matching approach (IOMMapReduce) in parallel. We investigate possible solutions to improve current object matching approaches with MapReduce, to make it support incrementality to speed up the deduplication process. Finally, our experimental evaluation on large data sets shows the high effectiveness and efficiency of the proposed approaches.
                          BiBTeX
                          @article {MaIncremental2014, title={Incremental Object Matching Approach of Schema-free Data with MapReduce}, author={Kun Ma and Fusen Dong and Bo Yang}, journal={International Journal of Computers and Applications}, pages={72-77}, year={2014}, volume={36}, number={2}}
                        • Ma, K., and Yang, B., "A Simple Scheme for Bibliography Acquisition using DOI Content Negotiation Proxy," The Electronic Library, 2014, 32 (6): 806-824 (WOS: 000346109800003, IF:0.436)
                          ISSN: 0264-0473, Date: 2014/11/3
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Purpose
                          – This paper aims to study a bibliography acquisition approach to verify the bibliography by author name from the integrated system and the metadata from the digital object identifier (DOI) content negotiation proxy. As managed scientific research papers increase at a higher rate, an accurate and automated scheme for bibliography acquisition is desirable.
                          Design/methodology/approach
                          – This study develops a framework using DOI content negotiation proxy as context for the entering of the bibliography. The bibliography acquisition architecture is composed of three point of views to reduce the complexity: terminal UI, service deployed in the cloud and DOI content negotiation proxy. To simplify the service interface and support many kinds of bibliographic formats, this paper presents the independent BibModel and its template-based model transformation engine to support rich bibliographic records.
                          Findings
                          – An important finding of this article is that we do some significant development work to combine the open CrossCite DOI content service and DOI resolvers of registry agencies. As more than 95 per cent of DOIs are owned or managed by CrossRef, DataCite, ISTIC and mEDTA DOI registry agencies, it is a common universal approach for the scientific research paper results with DOIs.
                          Practical implications
                          – Through a simple method built quickly from freely available parts, it is partially successful, suggesting the scheme can be integrated with third-party systems, such as the management system of scientific research results and the electronic journal management system. The analysis of the application’s effect shows the ability to verify the authenticity of the paper by author name from the system and the metadata from our DOI content negotiation proxy.
                          Originality/value
                          – This paper proposes an original and simple framework to acquire the metadata of bibliographies automatically. No detailed evaluative study has been carri
                          BiBTeX
                          @article {MaA2014, title={A simple scheme for bibliography acquisition using DOI content negotiation proxy}, author={Kun Ma and Bo Yang}, journal={The Electronic Library}, pages={806-824}, year={2014}, volume={32}, number={6}}
                        2013
                        
							
                        • Ma, K., "A Simple Model Transformation Approach Based on Textual Template," AISS: Advances in Information Sciences and Service Sciences, 2013, 5 (3): 676-683 
                          ISSN: 1976-3700, Date: 2013/2/15
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Model transformation is a focused area in Model-Driven Engineering (MDE). With the help of model transformation, it is possible to generate source codes, and target models from the source models. In this paper, we proposed a simple model transformation approach based on textual template. The proposed framework used a novel platform-specific business object model (PSM-BO) to sufficiently describe both the structural and behavioral properties of generic enterprise Web applications. In addition, the formalism of template and template statement was introduced to present the template-based model transformation rule. With the extension of template plugins, this approach can generate the codes of different target platforms. Finally a case study of Natural Science Foundation Management System was demonstrated how to build PSM-BOs and transform them into the final system. The distribution and uninterrupted running of this system proves that our approach is feasible and correct in practice.
                          BiBTeX
                        • Ma, K., and Sun, R., "Introducing WebSocket-based Real-time Monitoring System for Remote Intelligent Buildings," International Journal of Distributed Sensor Networks, 2013, 2013 (): 10 pages (EI: 20140817346647, WOS:000328757100001, IF: 0.923, Q3)
                          ISSN: 1550-1329, Date: 2013/12/1
                          SCImago Journal & Country Rank
                          acceptance rate: 39% 
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Today wireless sensor networks (WSNs) in electronic engineering are used in the monitoring of remote intelligent buildings, and the need for emerging Web 3.0 is becoming more and more in every aspect of electronic engineering. However, the key challenges of monitoring are the monitoring approaches and storage models of huge historical monitoring data. To address these limitations, we attempt to design a WebSocket-based real-time monitoring system for remote intelligent buildings. On one hand, we utilize the latest HTML5 WebSocket, Canvas and Chart technologies to monitor the sensor data collected in WSNs in the Web browser. The proposed monitoring system supports the latest HTML5 browsers and legacy browsers without native WebSocket capability transparently. On the other hand, we propose a storage model with lifecycle to optimize the NoSQL data warehouse. Finally, we have made the monitoring and storage experiments to illustrate the superiority of our approach. The monitoring experimental results show that the average latency time of our WebSocket monitoring is generally lower than polling, FlashSocket and Socket solution, and the storage experimental results show that our storage model has low redundancy rate, storage space and latency.
                          BiBTeX
                          @article {MaIntroducing2013, title={Introducing WebSocket-based Real-time Monitoring System for Remote Intelligent Buildings}, author={Kun Ma and Runyuan Sun}, journal={International Journal of Distributed Sensor Networks}, pages={10 pages}, year={2013}, volume={2013}, number={Article ID 867693}}
                        • Ma, K., Sun, R.,  and Abraham, A., "Toward a module-centralized and aspect-oriented monitoring framework in clouds," Journal of Universal Computer Science, 2013, 19 (15): 2241-2265 (WOS: 000329491500006, IF: 0.401)
                          ISSN: 0948-695X, Date: 2013/9/1
                          SCImago Journal & Country Rank
                          Scopus Document   Scopus Citeby   Abstract  BiBTeX
                           
                          Abstract
                          Currently, monitoring plays an important role in managing the Cloud computing environment. However, the Cloud computing owners and tenants often lack the management and monitoring tools to ensure the performance, robustness, dependability, and security. To address this limitation, this paper describes the development of a lightweight module-centralized and aspect-oriented monitoring framework. This framework performs end-to-end measurements at virtual and physical machine instances, software and Web service in the Cloud. It monitors the quality of service (QoS) parameters of the IaaS and SaaS layer in the forms of plug-in bundles. In addition, we discuss the manager-agent monitoring of entity objects and aspect-oriented Cloud service monitoring in detail. All the modules constitute the entire proposed framework to improve the performance in hybrid Clouds.
                          BiBTeX
                          @article {MaToward2013, title={Toward a module-centralized and aspect-oriented monitoring framework in clouds}, author={Kun Ma and Runyuan Sun and Ajith Abraham}, journal={Journal of Universal Computer Science}, pages={2241-2265}, year={2013}, volume={19}, number={15}}
                        2012
                        
							
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                        2010
                        
							
                        • Sun, R., Ma, K., Chen, Z., Peng, L., and Jing, S., "A Network Utilization Measurement Method Based Enhanced Maximum Traffic Accumulation," Journal of Northeastern University (Natural Science), 2010, 31 (2A): 254-257 (EI: 20111313875047)
                          ISSN: 1005-3026, Date: 
                          SCImago Journal & Country Rank
                          Scopus Document
                          •    Abstract  BiBTeX
                           
                          Abstract
                          BiBTeX
                        • Ma, K., Yang, B., Chen, Z., Li, Q., and Cui, L., "Research of Model-driven Web Application Rapid Development Platform," Computer Science, 2010, 37 (11A): 29-33 
                          ISSN: 1002-137X, Date: 
                             Abstract  BiBTeX
                           
                          Abstract
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                        Conference PapersTOP
                        
					
                        
						
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                          • Hui Liu, Ke Ji, Zhenxiang Chen, Kun Ma, Xiaofan Zhao, Malicious Attack Detection Method for Recommendation Systems Based on Meta-pseudo Labels and Dynamic Features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 14964, 8th International Joint Conference on Web and Big Data (APWeb-WAIM 2024), Jinhua, Zhejiang, Aug. 28-30, 2024, 379-393 (EI: )
                            Date: 
                          • Yongxin Yu, Ke Ji, Yuan Gao, Zhenxiang Chen, Kun Ma, Jun Wu, MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection, Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science 14649, Taipei, Taiwan, May 5-10, 2024, 28-39 (EI: )
                            Date: 2024/4/25
                          • Jiaqi Fang, Kun Ma*, SIGAN: Self-Inhibited Graph Attention Network FOR TEXT Classification, Proceedings of 2023 23th International Conference on Intelligent Systems Design and Applications (ISDA 2023), Olten, Switzerland, Dec. 11-13, 2023, 127-136 (EI: 20243216843570)
                            Date: 2024/7/15
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                          • Zhibing Yu, Kun Ma and Bo Yang, Core Point Paradigm and Evolution with Water Ripple Model, Proceedings of the 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2016), Fukuoka, Japan, Jul. 6-8, 2016, 357-362 (EI: 20170603321485, WOS:000391528700054)
                            Date: 2016/7/1
                          • Yang, Z., Ma, K.*, Zhong, J., Toward a Semantic Cache Supporting Version-based Consistency, Proceedings of 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2016), Fukuoka, Japan, Jul. 6-8, 2016, 367-372 (EI: 20170603321487, WOS:000391528700056)
                            Date: 2016/7/1
                          • Wu, J., Yang, B., Wang, L., Ma, K., Zhao, X., Zhou, J., MRF parameter estimation based on weighted Least Squares Fit method, Proceedings of 2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), Jin Zhou, China, Aug. 26-28, 2016, 164-169 (EI: 20164603016571)
                            Date: 2016/8/1
                          • Yu, Z., Yu, X., Chen, Y., and Ma, K., Distributed Top-k Keyword Search over Very Large Databases with MapReduce, Proceedings of 2016 IEEE International Congress on Big Data (BigData Congress 2016), San Francisco, USA, June 27-July 2, 2016, 349-352 (EI: 20164603008976, WOS:000390212200048)
                            Date: 2016/6/1
                          • Ma, K., Tang, Z., Zhong, J., Yang, B., LPSMon: A Stream-based Live Public Sentiment Monitoring System, Proceedings of The 17th International Conference on Web-Age Information Management, Part II, Lecture Notes in Computer Science , Nanchang, China, June 3-5, 2016, 9659: 534-536 (EI: 20162702570719, WOS:000389411500045, CCF-C)
                            Date: 2016/5/31
                            Acceptance Rate: 88/236=37.2%, Research Acceptance Rate: 80/219=36.5%, Demo Acceptance Rate: 8/17=47%
                            Demo details from here.
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                                            • Mostapha Zbakh and Kun Ma, Special Issue on Recent Advances in Big Data Management-PART 1, Recent Patents on Computer Science, 11 (3), 2018  CFP Published Issue
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                                            • Kun Ma and Ajith Abraham, Special Issue on Innovative Applications for the Cloud, International Journal of Grid and Utility Computing, 5 (4), 2014  CFP Published Issue
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                                                            • 实用新型（申请号：201320245920.0, 2013-05-09）, 授权日期：2014-03-06. 一种可转动毛刷. 申请人: 董富森, 发明人: 董富森, 马坤. (granted).
                                                            					
                                                            
				
                                                            
			
                                                            

           
                                                            
				
                                                            
					
                                                            
					
				
                                                            
				
                                                            
					
					
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                                                            2024
                                                            
							
                                                            • 软件著作权（申请号：2023R11L2313013, 2023-11-01; 2024SR0151838, 2024-01-23）. 小学数学应用题自动解答小程序. 著作权人: 济南大学, 联系人：衣禹桥、马坤.
                                                            • 软件著作权（申请号：2023R11L2637200, 2023-12-06; 2024SR0158303, 2024-01-24）. 基于低代码技术的图书销售管理系统. 著作权人: 济南大学, 联系人：马源、马坤.
                                                            • 软件著作权（申请号：2024R11L0743285, 2024-04-03; 2024SR0733085, 2024-05-29）. 动物识别专家系统. 著作权人: 济南大学, 联系人：孙铭、马坤.
                                                            • 软件著作权（申请号：2024R11L0748579, 2024-04-03; 2024SR0892147, 2024-06-28）. 坦克大战游戏系. 著作权人: 济南大学, 联系人：路苗苗、马坤.
                                                            • 软件著作权（申请号：2024R11L1037907, 2024-05-06; 2024SR0924608, 2024-07-03）. 基于低代码技术的网络事实核查系统（谣无音讯）. 著作权人: 济南大学, 联系人：方家琪、马坤.
                                                            • 软件著作权（申请号：2024R11L0756059, 2024-04-03; 2024SR1033036, 2024-07-22）. 高考智选志愿填报系统. 著作权人: 济南大学, 联系人：徐乐轩、马坤.
                                                            • 软件著作权（申请号：2024R11L1503219, 2024-06-23; 2024SR1162747, 2024-08-12）. 趣乡音微信小程序. 著作权人: 济南大学, 联系人：赵孟豪、马坤.
                                                            2023
                                                            
							
                                                            • 软件著作权（申请号：2023R11L0873303, 2023-04-12; 2023SR0795272, 2023-07-04）. 假新闻识别系统. 著作权人: 济南大学, 联系人：齐世豪、马坤.
                                                            • 软件著作权（申请号：2023R11L0873311, 2023-05-18; 2023SR0795273, 2023-07-04）. 基于图注意力网络和外部知识的假新闻检测系统. 著作权人: 济南大学, 联系人：孙志杰、马坤.
                                                            • 软件著作权（申请号：2023R11L0611189, 2023-04-12; 2023SR0627549, 2023-06-12）. 济大导游解说小程序. 著作权人: 济南大学, 联系人：刘延松、马坤.
                                                            • 软件著作权（申请号：2023R11L0610976, 2023-04-12; 2023SR0627548, 2023-06-12）. 魅力齐鲁-山东非遗数字馆微信小程序. 著作权人: 济南大学, 联系人：王阳、马坤.
                                                            • 软件著作权（申请号：2023R11L0873292, 2023-05-18; 2023SR0795275, 2023-07-04）. 基于低代码技术的新闻检测系统. 著作权人: 济南大学, 联系人：方家琪、马坤.
                                                            2022
                                                            
							
                                                            • 软件著作权（申请号：2021R11L3283139, 2021-11-18; 2022SR0079912, 2022-01-12）. 大学生日常时间管理系统. 著作权人: 济南大学, 联系人：刘伊康、马坤.
                                                            • 软件著作权（申请号：2022R11L0936387, 2022-05-28; 2022SR1129633, 2022-08-15）. 舆情检测管理系统. 著作权人: 济南大学, 联系人：郑坤昌、马坤.
                                                            2021
                                                            
							
                                                            • 软件著作权（申请号：2021R11L1904798, 2021-05-28; 2021SR1100235, 2021-07-26）. 口罩佩戴识别检测系统. 著作权人: 济南大学, 联系人：张国辉、马坤.
                                                            • 软件著作权（申请号：2021R11L1910729, 2021-05-30; 2021SR1100234, 2021-07-26）. 论文网络的论文影响力可视化系统. 著作权人: 济南大学, 联系人：鲁岳、刘方涵、马坤.
                                                            • 软件著作权（申请号：2021R11L1901748, 2021-05-28; 2021SR1211048, 2021-08-16）. 微顶流小程序. 著作权人: 济南大学, 联系人：迟恒喆、马坤.
                                                            • 软件著作权（申请号：2021R11L2049592, 2021-06-21; 2021SR1219190, 2021-08-17）. 基于短文本的微博热点情感分析系统. 著作权人: 济南大学, 联系人：陈静、于佳、马坤.
                                                            • 软件著作权（申请号：2021R11L2049586, 2021-06-21; 2021SR1219189, 2021-08-17）. 社交媒体虚假新闻检测系统. 著作权人: 济南大学, 联系人：汤长昊、姚胤楠、马坤.
                                                            2020
                                                            
							
                                                            • 软件著作权（申请号：2020R11L643454, 2020-05-21; 2020SR0832205, 2020-07-27）. 舆情监控系统. 著作权人: 济南大学, 联系人：段平碧、马坤.
                                                            • 软件著作权（申请号：2020R11L643416, 2020-05-20; 2020SR0832352, 2020-07-27）. 假新闻检测系统. 著作权人: 济南大学, 联系人：李松谦、马坤.
                                                            • 软件著作权（申请号：2020R11L643425, 2020-05-20; 2020SR0832226, 2020-07-27）. 垃圾分类小程序. 著作权人: 济南大学, 联系人：何睿康、马坤.
                                                            • 软件著作权（申请号：2019R11L1944233, 2019-12-20; 2020SR0257216, 2020-03-26）. 小智签系统. 著作权人: 济南大学, 联系人：张方略、马坤.
                                                            • 软件著作权（申请号：2020R11L643439, 2020-06-23; 2020SR0952644, 2020-08-19）. 学习助理系统. 著作权人: 济南大学, 联系人：王玉晓、马坤.
                                                            • 软件著作权（申请号：2020R11L878050, 2020-06-23; 2020SR0973041, 2020-08-24）. 微舆情系统. 著作权人: 济南大学, 联系人：吴磊、马坤.
                                                            • 软件著作权（申请号：2020R11L2120951, 2020-09-29; 2020SR1713166, 2020-12-02）. 多模态垃圾分类小程序. 著作权人: 济南大学, 联系人：苏南、马坤.
                                                            2019
                                                            
							
                                                            • 软件著作权（申请号：2019R11L634195, 2019-05-13; 2019SR0738460, 2019-07-17）. 文献推荐系统. 著作权人: 济南大学, 联系人：刘方涵、马坤.
                                                            • 软件著作权（申请号：2019R11L740915, 2019-05-29; 2019SR0831304, 2019-08-09）. 自匹配失物招领系统. 著作权人: 济南大学, 联系人：姚胤楠、马坤.
                                                            • 软件著作权（申请号：2019R11L1005754, 2019-07-18; 2019SR0975757, 2019-09-20）. 数据可视化系统. 著作权人: 济南大学, 联系人：马坤.
                                                            • 软件著作权（申请号：2019R11L1005821, 2019-07-18; 2019SR0975180, 2019-09-20）. 软件工程实训跟踪指导系统. 著作权人: 济南大学, 联系人：马坤.
                                                            2018
                                                            
							
                                                            • 软件著作权（申请号：2017R11L936464, 2017-10-24; 2018SR063982, 2018-01-25）. 晒米约拍系统	. 著作权人: 济南大学, 联系人：牛学蔚、马坤.
                                                            • 软件著作权（申请号：2018R11S836876, 2018-10-16; 2018SR940876, 2018-11-26）. OA办公系统. 著作权人: 济南大学, 联系人：李松谦、马坤.
                                                            2017
                                                            
							
                                                            • 软件著作权（申请号：2017R11L262263, 2017-04-08; 2017SR473062, 2017-08-28）. 基于密码学的移动终端文件保护软件. 著作权人: 济南大学, 联系人：罗天任、马坤.
                                                            • 软件著作权（申请号：2017R11L173037, 2017-03-15; 2017SR381599, 2017-07-19）. 基于无线局域网的二维码智能扫描点餐系统. 著作权人: 济南大学, 联系人：罗天任、马坤.
                                                            • 软件著作权（申请号：2017R11L937329, 2017-10-11; 2017SR738309, 2017-12-27）. 网络舆情监测系统. 著作权人: 济南大学, 联系人：马坤、牛学蔚.
                                                            2016
                                                            
							
                                                              2015
                                                              
							
                                                              • 软件著作权（申请号：2014R11L381843, 2014-12-03; 2015SR066396, 2015-04-22）. 艾特我智能校园系统. 著作权人: 济南大学, 联系人：马坤.
                                                              • 软件著作权（申请号：2014R11L381819, 2014-12-03; 2015SR044942, 2015-03-13）. 学位论文评审管理系统. 著作权人: 济南大学, 联系人：马坤.
                                                              • 软件著作权（申请号：2015R11L346116, 2015-10-07; 2015SR263003, 2015-12-16）. 面向服务的多租户通用微信公众系统 . 著作权人: 济南大学, 联系人：马坤.
                                                              • 软件著作权（申请号：2015R11L346113, 2015-10-07; 2015SR263158, 2015-12-16）. 面向服务的多用户大规模开放在线课程系统 . 著作权人: 济南大学, 联系人：马坤.
                                                              • 软件著作权（申请号：2015R11L346108, 2015-10-07; 2015SR264722, 2015-12-17）. 模块化社交管理系统. 著作权人: 济南大学, 联系人：马坤、唐子杰.
                                                              • 软件著作权（申请号：2015R11L346107, 2015-10-07; 2015SR261885, 2015-12-16）. 模块化内容管理系统（UJNCMS）. 著作权人: 济南大学, 联系人：马坤、唐子杰.
                                                              2014
                                                              
							
                                                              • 软件著作权（申请号：2013R11L267810, 2013-12-11; 2014SR040751, 2014-04-10）. 国际化学术团队会员管理系统. 著作权人: 济南大学, 联系人：马坤.
                                                              • 软件著作权（申请号：2013R11L267786, 2013-12-11; 2014SR041170, 2014-04-10）. 基于版本控制的软件在线升级系统. 著作权人: 济南大学, 联系人：马坤.
                                                              • 软件著作权（申请号：2013R11L267720, 2013-12-11; 2014SR040968, 2014-04-10）. 艾特我互助社交平台. 著作权人: 济南大学, 联系人：马坤.
                                                              • 软件著作权（申请号：2013R11L267753, 2013-12-11; 2014SR041153, 2014-04-10）. RSS魔方资讯订阅系统. 著作权人: 济南大学, 联系人：马坤.
                                                              2013
                                                              
							
                                                              • 软件著作权（申请号：2013R11L123749, 2013-07-03; 2013SR118729, 2013-11-04）. 基于DOI的文献元数据抽取系统. 著作权人: 济南大学, 联系人：马坤.
                                                              • 软件著作权（申请号：, ; 2013SR075316, 2013-07-27）. 多平台虚拟化环境集成管理系统. 著作权人: 济南大学, 联系人：马坤.
                                                              										
					
                                                              
				
                                                              
			
                                                              


			
                                                              
				
                                                              
					
                                                              
					
				
                                                              
				
                                                              
					
					
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