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作者： Wang, Xue Zhou, Xuan Wang, Shan School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China

Schema summarization on large-scale databases is a challenge. In a typical large database schema, a great proportion of the tables are closely connected through a few high degree tables. It is thus dificult to separate these tables into clusters that represent different topics. Moreover, as a schema can be very big, the schema summary needs to be structured into multiple levels, to further improve the usability. In this paper, we introduce a new schema summarization approach utilizing the techniques of community detection in social networks. Our approach contains three steps. First, we use a community detection algorithm to divide a database schema into subject groups, each representing a specific subject. Second, we cluster the subject groups into abstract domains to form a multi-level navigation structure. Third, we discover representative tables in each cluster to label the schema summary. We evaluate our approach on Freebase, a real world large-scale database. The results show that our approach can identify subject groups precisely. The generated abstract schema layers are very helpful for users to explore database. © 2012 Springer Science+Business Media, LLC & Science Press, China.

关键词： database systems

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A fast algorithm to building a fuzzy rough classifier 13

A fast algorithm to building a fuzzy rough classifier

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13th International Conference on Machine Learning and Cybernetics, ICMLC 2014

作者： Tsang, Eric C. C. Zhao, Suyun The Macau University of Science and Technology Taipa Macau China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing100872 China

ISBN: (纸本)9783662456514

In this paper, by strict mathematic reasoning, we discover the relation between the similarity relation and lower approximation. Based on this relation, we design a fast algorithm to build a rule based fuzzy rough classifier. Finally, the numerical experiments demonstrate the efficiency and the affectivity of the proposed algorithm. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Rough set theory

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RETRACTED ARTICLE: knowledge management technologies and applications: A literature review

RETRACTED ARTICLE: Knowledge management technologies and app...

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作者： An, Xiaomi Wang, Wang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Information Resource Management Renmin University of China Beijing China

ISBN: (纸本)9781424469291

knowledge management (KM) technologies and applications are fundamental to the success of KM programs. This paper aims to find out linkages between newly-developed KM theories and various technologies and their relevant applications. It explores various understandings on KM technologies, their functions and benefits to organizations, and several types of classification of KM technologies and their applications by reviewing literature. The authors ask and answer the following questions: (1) How are KM technologies defined in literature according to their different perspectives or disciplines? (2) What are the functions, or the roles, of the KM technologies and their applications as enabling KM? and (3) How are KM technologies classified in literature? This study may have implications to mapping, building and operating KM programs that need to have concerns about KM technologies. Different schools of thought on KM technologies, their functions and benefits and their strengths and limitations as solutions to KM problems shall be taken into account. © 2010 IEEE.

关键词： knowledge management

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Dynamic balanced storage in wireless sensor networks 07

Dynamic balanced storage in wireless sensor networks

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DMSN '07: 4th Workshop on data Management for Sensor Networks

作者： Lai, Yongxuan Chen, Hong Wang, Yufeng School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE No.59 Zhongguancun Street Haidian District Beijing China

ISBN: (纸本)9781595939111

data-centric storage is an effective and important technique in the wireless sensor networks. It stores the sensing data according to their values by mapping them to some point in the network in order to avoid routing all the values outside the network and flooding the queries. However, in most data-centric storage schemes, there is a "hotspot" problem due to the skewness of data and randomness of the mapping functions. Large number of sensor readings (events) may be routed to the same point by the predefined hashed function. In this paper, we propose a new Dynamic BAlanced data-centric Storage (DBAS) scheme, a cooperative strategy between the base station and the in-network processing in wireless sensor network. Our scheme, which utilizes the rich resources in the base station and is aware of the data distributions of the network, dynamically adjusts the mappings from readings to the storage points to balance the storage and workload in the network, as well as to reduce the cost of storing these readings. Moreover, it takes advantage of perimeter routing algorithm of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that DBAS is more balanced and energy efficient than the traditional data-centric storage mechanism in wireless sensor network.

关键词： Wireless sensor networks

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Multi-fuzzy-objective graph pattern matching with big graph data

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Journal of database Management 2019年第4期30卷 24-40页

作者： Li, Lei Zhang, Fang Liu, Guanfeng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Zhongxing Telecommunication Equipment Corporation Nanjing China Macquarie University Sydney Australia

Big graph data is different from traditional data and they usually contain complex relationships and multiple attributes. With the help of graph pattern matching, a pattern graph can be designed, satisfying special personal requirements and locate the subgraphs which match the required pattern. Then, how to locate a graph pattern with better attribute values in the big graph effectively and efficiently becomes a key problem to analyze and deal with big graph data, especially for a specific domain. This article introduces fuzziness into graph pattern matching. Then, a genetic algorithm, specifically an NSGA-II algorithm, and a particle swarm optimization algorithm are adopted for multifuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively. © 2019,IGI Global.

关键词： Genetic algorithms

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A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence

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Journal of data and Information Science 2019年第4期4卷 13-25页

作者： Jane H.Qin Jean J.Wang Fred Y.Ye Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China International Joint Informatics Laboratory(IJIL) Nanjing University-University of IllinoisNanjing-ChampaignChina-USA

Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic ***/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric ***:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each *** limitations:All analyses use keywords,without any other *** implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical ***/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.

关键词： Keyword co-occurrence Network analysis Information visualization Biomedicine Hot topics CRISPR-Cas iPS cell Synthetic biology

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Online Streaming Features Causal Discovery Algorithm Based on Partial Rank Correlation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 197-208页

作者： Yang, Jing Jiang, Liufeng Shen, Anbo Wang, Aiguo Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of Electronic and Information Engineering Foshan University Foshan528011 China

Aimedat the problem of dynamic causal discovery in the era of artificial intelligence, this article combines partial rank correlation coefficients and streaming features in the field of Bayesian network structure learning and proposes a new online streaming feature causal discovery algorithm based on partial rank correlation named the partial rank casual discovery streaming feature based algorithm. This algorithm is not only suitable for Bayesian causal structure learning in dynamic feature spaces generated by sequential streams of features but can also effectively process multivariate linear Gaussian and nonlinear non-Gaussian data. We present three main contributions. First, for arbitrarily distributed datasets, which can be generated by the additive noise model, we proved that the partial rank correlation coefficient can be used as the criterion for the conditional independence test and explored the distribution of corresponding statistics. Second, the PCSDSF algorithm redefined the relevance based on partial rank statistic prospects and then redefined conditional dependence or independence. This method can significantly reduce the number of conditional independence tests and achieves a good time performance. Finally, theoretical analysis and several experiments proved the reliability of the algorithm. A simulation showed that on average, the PCSDSF algorithm outperforms existing algorithms in terms of both the accuracy and time performance. © 2022 IEEE.

关键词： Additive noise

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HDROP: Detecting ROP attacks using performance monitoring counters

HDROP: Detecting ROP attacks using performance monitoring co...

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10th International Conference on Information Security Practice and Experience, ISPEC 2014

作者： Zhou, Hongwei Wu, Xin Shi, Wenchang Yuan, Jinhui Liang, Bin Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Information Engineering University Zhengzhou China

ISBN: (纸本)9783319063195

Combining short instruction sequences originated only from existing code pieces, Return Oriented Programming (ROP) attacks can bypass the code-integrity effort model. To defeat this kind of attacks, current approaches check every instruction executed on a processor, which results in heavy performance overheads. In this paper, we propose an innovative approach, called HDROP, to detecting the attacks. It utilizes the observation that ROP attacks often make branch predictor in modern processors fail to determine the accurate branch destination. With the support of PMC (Performance Monitoring Counters) that is capable of counting performance events, we catch the abnormal increase in branch mis-prediction and detect the existence of ROP attacks. In HDROP, each basic unit being checked consists of hundreds of instructions rather than a single one, which effectively avoids significant performance overheads. The prototype system we developed on commodity hardware shows that HDROP succeeds in detecting ROP attacks, and the performance tests demonstrate that our approach has acceptably lower overheads. © 2014 Springer International Publishing.

关键词： Computers

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A study of SQL-on-Hadoop systems

A study of SQL-on-Hadoop systems

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4th and 5th Workshops on Big data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 2014

作者： Chen, Yueguo Qin, Xiongpai Bian, Haoqiong Chen, Jun Dong, Zhaoan Du, Xiaoyong Gao, Yanjie Liu, Dehai Lu, Jiaheng Zhang, Huijie Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)9783319130200

Hadoop is now the de facto standard for storing and processing big data, not only for unstructured data but also for some structured data. As a result, providing SQL analysis functionality to the big data resided in HDFS becomes more and more important. Hive is a pioneer system that support SQL-like analysis to the data in HDFS. However, the performance of Hive is not satisfactory for many applications. This leads to the quick emergence of dozens of SQL-on-Hadoop systems that try to support interactive SQL query processing to the data stored in HDFS. This paper firstly gives a brief technical review on recent efforts of SQLon- Hadoop systems. Then we test and compare the performance of five representative SQL-on-Hadoop systems, based on some queries selected or derived from the TPC-DS benchmark. According to the results, we show that such systems can benefit more from the applications of many parallel query processing techniques that have been widely studied in the traditional MPP analytical databases. © Springer International Publishing Switzerland 2014.

关键词： Big data

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Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

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China Communications 2021年第2期18卷 259-270页

作者： Jinhua Pan Lusheng Wang Hai Lin Zhiheng Zha Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

To support dramatically increased traffic loads,communication networks become *** cell association(CA)schemes are timeconsuming,forcing researchers to seek fast *** paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is *** the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until *** the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user *** demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional ***,performance metrics,such as capacity and fairness,can be guaranteed.

关键词： ultra-dense cellular networks(UDCN) cell association(CA) deep Q-learning proportional fairness Q-learning

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