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

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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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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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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A method for measuring journal discriminative capacity and its application in WOS 15th

A method for measuring journal discriminative capacity and i...

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15th International Conference on Sustainable Digital Communities, iConference 2020

作者： Wang, Hao Zhang, Baolong Deng, Sanhong Su, Xinning School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China

ISBN: (纸本)9783030436865

Journal discriminative capacity refers to the degree of difference between the journals in research subjects, and is of great significance for detecting the level of journal differentiation. Current research on journal discrepancies is predominantly focused on the quantitative analysis of journal content, and rarely measures the degree of journal difference. To address this lapse in research, a method from the perspective of difference is proposed in this paper to quantitatively measure and analyze the discriminative capacity of journals. Using the bibliographic of Science Citation Index (SCI), Social Science Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI) journals from 2015–2017 as the data source, the contents differences of 23 Library and Information Science (LIS) journals are analyzed through hierarchical clustering and journal discriminative capacity (JDC) measurement. The discriminative capacity of disciplines is then calculated and combined with multidimensional scaling analysis to detect the difference characteristics of journals in ten disciplines, and the mean JDC is used to explore the overall differences of disciplines. Results show that the discriminative capacity of LIS journals is obviously stratified, the journals of library science are the most discriminating, and the journals of evaluation and review are the weakest. In addition, the journals of different disciplines have distinct disciplinary characteristics, with the discriminative capacity of SCI journals as the most notable, followed by SSCI journals, and with A&HCI journals displaying the weakest discriminative capacity. © Springer Nature Switzerland AG 2020.

关键词： Digital libraries

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A Semantic Framework for Chinese Historical Events Based on Linked data and knowledge Graph 16th

A Semantic Framework for Chinese Historical Events Based on ...

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16th International Conference on Diversity, Divergence, Dialogue, iConference 2021

作者： Wang, Hao Li, Yueyan Deng, Sanhong School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China

ISBN: (纸本)9783030712914

In the era of big data, data redundancy has become an obstacle to deep reading. The objective of linked data as a new data organization model is to transform data into structured data following unified standards. The lack of Chinese conceptual terms has seriously hindered the semantization and standardization of Chinese domain ontology. Taking Chinese historical events as an example, ontology technology is used in this paper to standardize the definition of concepts and semantic relations in domain knowledge. Moreover, concepts from text resources are extracted through a deep learning algorithm Bi-LSTM-CRFs and combined with an external knowledge base to realize the fusion of related data within various data sets. Ultimately, the knowledge ontology of historical events is displayed in the way of a knowledge graph to further explore the practical application. The results show that the accuracy of the terms extraction of historical events is about 80% indicating the good recognition performance and portability of the model. © 2021, Springer Nature Switzerland AG.

关键词： Linked data

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A hardware sandbox using processor virtualization for untrusted native code

A hardware sandbox using processor virtualization for untrus...

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2013 International Forum on Computer and Information Technology, IFCIT 2013

作者： Wu, Xin Shi, Wen Chang Liang, Bin Key Laboratory of Data Engineering and Knowledge Engineering Renmin University Ministry Of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9783038350194

Native code which is in the form of browser plugins and native binaries in browser extensions brings significant security threats and has attracted considerable attention in recent years. Many researches focus on dangerous behaviors in browser extensions for new security countermeasures but there is no fundamental improvement. Constructing an individual isolation domain and providing sandboxed execution of the code is a key idea to address this problem. However current approach relies heavily on the trust of a module executing within the context of the whole untrusted browser code base accompanied with inevitable vulnerabilities. In this paper, we propose an innovative sandbox utilizing the hardware virtualization support available in modern Intel and AMD processors to construct an individual isolation domain for each benign-but-buggy native code. Our approach push the responsibilities for security down to the hardware level and minimize the difficulties encountered with software based approaches. By utilizing the processor instruction VMENTER or VMEXIT to cause the context switch, we can obtain a better system performance. © (2014) Trans Tech Publications, Switzerland.

关键词： Virtual reality

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Author Name Disambiguation Based on Rule and Graph Model 9th

Author Name Disambiguation Based on Rule and Graph Model

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9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Zhang, Lizhi Ban, Zhijie Inner Mongilia A.R. Key Laboratory of Data Mining and Knowledge Engineering College of Computer Science Inner Mongolia University Huhhot China

ISBN: (纸本)9783030604493

Author name disambiguation has long been viewed as a challenging problem in scientific literature management, and with the substantial growth of the scientific literature, the solution to this problem has become increasingly difficult and urgency. In this paper, we conduct research on the author name disambiguation problem in large-scale academic papers. In our method, we combine the paper feature information and the relation information between the papers for disambiguation. Based on the Aminers disambiguation framework, we present a novel method to constructing the paper relation graph based on atomic cluster and propose an efficient post processing algorithm, aiming to improve the disambiguation performance by rule-based clustering, this algorithm utilizes similarity features based on metadata information and implement two types of disambiguation rules. We carefully evaluate the proposed disambiguation method on real-world large data and experimental result shows that our method achieves clearly better performance than the state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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