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Deprank: A probabilistic measure of dependence via heterogeneous links

Deprank: A probabilistic measure of dependence via heterogen...

引用

Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Li, Pei Hu, Bo Liu, Hongyan He, Jun Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Dependence is a common relationship between objects. Many works have paid their attentions on dependence, but many of them mainly focus on constructing or exploiting dependence graphs on some specific domain. In this paper, we give a generic definition of dependence and make equivalence to the situation of graphs, and propose an algorithm called DepRank, which quantifies the dependence degree of a node pair based on probabilities of all dependence paths. Empirical study shows that DepRank method has reasonable results and maintains a well balance between accuracy and efficiency. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Artificial intelligence

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AutoPCS: A phrase-based text categorization system for similar texts

AutoPCS: A phrase-based text categorization system for simil...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Li, Zhixu Li, Pei Wei, Wei Liu, Hongyan He, Jun Liu, Tao Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Nearly all text classification methods classify texts into predefined categories according to the terms appeared in texts. State-of-the-art of text classification prefer to simplely take a word as a term since it performs good on some famous datasets;some experts even pointed out that phrases don't improve or improve only marginally the classifiction accuracy. However, we found out that this is not always true when we try to categorize texts about similar topics in the same domain. With words only we can not categorize those texts effectively since they nearly share the same word set. Then we suppose the results might be improved if we also use phrases as terms. To testify our supposition, we propose our own phrase extraction way as well as select proper feature selection method and classifier by conducting experimental study on a data set which comes from paper abstracts in the field of databases. Accordingly, we also develop a system called AutoPCS which can be used to help experts in choosing relevant topics for newly coming papers from a predefined topic list only by their abstracts. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Classification (of information)

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H-tree: A hybrid structure for confidence computation in probabilistic databases

引用

14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Zhang, Qian Qin, Biao Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9783642292521

Probabilistic database has become a popular tool for uncertain data management. Most work in the area is focused on efficient query processing and has two main directions, accurate or approximate evaluation. In recent work for conjunctive query without self-joins on a tuple-independent probabilistic database, query evaluation is equivalent to computing marginal probabilities of boolean formulas associated with query results. If formulas can be factorized into a read-once form where every variable appears at most once, confidence computation is reduced to a tractable problem that can be evaluated in linear time. Otherwise, it is regarded as a NP-hard problem and need to be evaluated approximately. In this paper, we propose a framework that evaluates both tractable and NP-hard conjunctive queries efficiently. First, we develop a novel structure H-tree, where boolean formulas are decomposed to small partitions which are either read-once or NP-hard. Then we propose algorithms for building H-tree and parallelizing (approximate) confidence computation. We also propose fundamental theorems to ensure the correctness of our approaches. Performance experiments demonstrate the benefits of H-tree, especially for approximate confidence evaluation on NP-hard queries. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Query processing

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A neural network based forecasting method for the unemployment rate prediction using the search engine query data

A neural network based forecasting method for the unemployme...

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2011 8th IEEE International Conference on e-Business engineering, ICEBE 2011

作者： Xu, Wei Zheng, Tingting Li, Ziang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769545189

Unemployment rate prediction has become critically important, because it can help government to make decision and design policies. In recent years, forecast of unemployment rate attracts much attention from governments, organizations, and research institutes, and researchers. Recently, a novel method using search engine query data to forecast unemployment was proposed by scholars. In this paper, a data mining based framework using web information is introduced for unemployment rate prediction. Under the framework, a neural network method, as one of the most effective data mining tools, is developed to forecast unemployment trend using search engine query data. In the proposed method, search engine query data related with employment activities is firstly found. Secondly, feature selection models including correlation coefficient method and genetic algorithm are constructed to reduce the dimension of the query data. Thirdly, various neural networks are employed to model the relationship between unemployment rate data and query data. Fourthly, an optimal neural network is selected as the selective predictor by using the cross-validation method. Finally, the selective neural network predictor with the best feature subset is used to forecast unemployment trend. The empirical results show that the proposed method clearly outperforms the classical forecasting approaches for the unemployment rate prediction. These findings imply that data mining method, such as neural networks, together with web information, can be used as an alternative tool to forecast social/economic hotspot. © 2011 IEEE.

关键词： Forecasting

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Optimizing queries with expensive video predicates based on estimation of attribute cardinality

Optimizing queries with expensive video predicates based on ...

引用

International Conference on Multimedia Information Networking and Security

作者： Yu, Lisheng Zhang, Jianmei Wang, Shan Key Laboratory of Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769542584

With rapid advances in video processing technologies, video data increased rapidly and becomes popular in our daily life for both professional and consumer applications, e.g., surveillance, education, entertainment. Such needs require the data management system not only can store and access the video content, but also able to optimize the queries with expensive video predications in an effective and efficient way. In previous research literature, query optimizations in relational database systems (RDBMS) are often based on disk I/O cost of involved operations. However, for a query that contains expensive video predicates, traditional cost estimation model does not work well. Although researchers have proposed some approaches which can solve the problem in certain situations, there are still some unresolved issues, and it needs further optimization. In this paper, motivate from a real-world large supermarket's business data and video surveillance data management scenario, through considering the characteristics of video data and its expensive processing, we propose a novel query optimization approach that caches operators' results and reconstructs the join order based estimation for attribute cardinality. This approach reduces the invoking times of expensive video predicate in a greater degree and gives a better solution for mixed query optimization which contains traditional data types and large object operations. The experiment result is satisfactory while compare with existing expensive predicates query optimization methods. © 2010 IEEE.

关键词： Relational database systems

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RETRACTED ARTICLE: Studies of knowledge management methods from literature

RETRACTED ARTICLE: Studies of knowledge management methods f...

引用

作者： Zhang, Jing Ye, Liuqi Business School of Hohai University Nanjing Jiangsu 210098 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing 100872 China

ISBN: (纸本)9781424469291

knowledge management methods reflect ways to undertake knowledge management objectives and actions taken for implementation. However, little has been written on the topic. The paper summarizes main topics of knowledge management methods from literature, including research contents, management methods, main types, objects involved, study domains, perspectives, objectives and tasks, school and achieving channels, developing trends and knowledge management methodology and etc. © 2010 IEEE.

关键词： knowledge management

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The internet of things and personal privacy protection

The internet of things and personal privacy protection

引用

2010 International Conference of Logistics engineering and Management: Logistics for Sustained Economic Development - Infrastructure, Information, Integration, ICLEM 2010

作者： Zhang, Jing Ye, Liuqi Business School Hohai University Nanjing Jiangsu 210098 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing 100872 China

ISBN: (纸本)9780784411391

Protecting personal privacy and its right are a respect for the human rights, the necessary condition for healthy development of a democratic society and the important criteria of maintaining the basic dignity to be human. The internet of things with its ubiquitous data-aware and intelligent information processing capabilities, improves social efficiency and severe challenges for information security and privacy. In this environment, how to ensure information security and privacy are a major breakthrough in one of the obstacles and an important issue that must be addressed and resolved in the developing process of the internet of things. Thus, in the development of it, we should follow the basic principles of protecting personal privacy such as the entire management, legal compliance, security, adding information, balance and accountability, and establish such models of protecting personal privacy as laws, industry self-regulation and technology to fully protect the inviolability of personal privacy, and remove obstacles and barriers for the applications of the internet of things. © 2010 ASCE.

关键词： Logistics

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Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension 2

Extremely Short Chinese Text Classification Method Based on ...

引用

2019 2nd International Symposium on Big data and Applied Statistics, ISBDAS 2019

作者： Yue, Yongzeng Zhang, Yuhong Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology] Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei23061 China

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness;secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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RETRACTED ARTICLE: System formulation of knowledge sharing and exchange in the organization

RETRACTED ARTICLE: System formulation of knowledge sharing a...

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ISBN: (纸本)9781424453269

knowledge resource is becoming the most important in an organization, Its exploring degree has direct influence on the core competitive power of the organization. System is also an integral part, the basis and protection for keeping an organization working well, thus the organization realizes its knowledge sharing and exchange and knowledge resource exploring by establishing the group learning system, regular meeting system, the knowledge report system and the knowledge publicity system. Then knowledge can be transmitted widely in the organization and generate knowledge contributions, innovations and promote the core competitive power. © 2010 IEEE.

关键词： knowledge management

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A support vector machine based method for credit risk assessment

A support vector machine based method for credit risk assess...

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International Workshop on Cloud Services-Platform Accelerating E-Business

作者： Xu, Wei Zhou, Shenghu Duan, Dongmei Chen, Yanhui Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769542270

The credit card industry has been growing rapidly in recent years, and credit risk assessment becomes critically important for financial companies. In this paper, a novel support vector machine (SVM) based ensemble model is proposed for credit risk assessment. In the proposed method, principles component analysis (PCA) is firstly employed for credit feature selection. Secondly, SVMs with different kernels are trained by using genetic algorithm (GA) to optimize the parameters, and the corresponding assessment results are obtained. Thirdly, all results produced by different SVMs are combined by several ensemble strategies. Finally, an optimal ensemble strategy is selected for credit scoring. For validation, two real world credit datasets are used to test the effectiveness and efficiency of our proposed method. The experiment results find that our proposed ensemble model outperforms commonly used credit scoring tools. The findings of the study reveal the support vector machine based ensemble method to be a promising alternative for credit scoring. © 2010 IEEE.

关键词： Support vector machines

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