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4th International Conference on Advanced data Mining and Applications, ADMA 2008

作者： Li, Haifeng Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)3540881913

Closed frequent itemsets(CFI) mining uses less memory to store the entire information of frequent itemsets thus is much suitable for mining stream. In this paper, we discuss recent CFI mining methods over stream and presents an improved algorithm Moment+ based on the existent one Moment. Moment+ focuses on the problem of mining CFI over data stream sliding window and proposes a new structure Extended Closed Enumeration Tree(ECET) to store the CFIs and nodes' BPN which is introduced to reduce the search space, with which new mining method is designed to mine more rapidly with little memory cost sacrifice. The experimental results show that this method is effective and efficient. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Mining

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Evolving sequential patterns mining model over click stream with Levenshtein-Automata

Evolving sequential patterns mining model over click stream ...

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3rd International Conference on Innovative Computing Information and Control, ICICIC'08

作者： Li, Haifeng Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769531618

Sequential pattern mining is an important problem in continuous, fast, dynamic and unlimited stream mining. Recently approximate mining algorithms are proposed which spend too many system resources and can only obtain the partial feature of stream. In this paper, a multi-level evolving sequential pattern mining model ESPMM is presented to address this problem thus the mostly entire stream feature is obtained. Furthermore, because of the smaller support of sequential patterns in each level, a mining method BMLA based on Levenshtein-Automata is proposed which builds state conversion model to compute sequences' similarity in linear time. The experiment results show this model is effective and efficient. © 2008 IEEE.

关键词： Automata theory

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基于Web Services的信息系统的实施研究

基于Web Services的信息系统的实施研究

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第15届海峡两岸信息管理发展与策略学术研讨会(2009)

作者： Zuo Meiyun 左美云 Wang Shijuan 王世娟 In formation School and Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin Univ 中国人民大学信息学院数据工程与知识工程教育部重点实验室北京

基于Web Services的信息系统将会是今后信息系统建设的一种重要模式。论文在对23个实施基于WebServices的信息系统的二手资料分析的基础上，归纳了基于Web Services的信息系统的类型、实施策略和实施过程。

关键词：企业管理信息系统控制策略网络服务

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LOB: Bucket based index for range queries

LOB: Bucket based index for range queries

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9th International Conference on Web-Age Information Management, WAIM 2008

作者： Wang, Jieping Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769531854

database-as-a-Service is a promising data management paradigm in which data is encrypted before being sent to the untrusted server. Efficient querying on encrypted data is a performance critical problem which has various solutions, among which bucket based index is an effective and flexible one. In previous research some metrics are proposed to measure security and efficiency. In this paper, we illustrate by example the limitations of these metrics and introduce a new security metric based on probability distribution variance and efficiency metric based on overlapping ratio. Based on these metrics we propose a local overlapping bucket algorithm (LOB) with time complexity of O(nlogn), where n represents the cardinality of the table. Experiments on synthetic and real dataset show that our algorithm can achieve higher security by trading off efficiency. © 2008 IEEE.

关键词： Information management

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Using ontology to enhance collaborative recommendation based on community

Using ontology to enhance collaborative recommendation based...

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9th International Conference on Web-Age Information Management, WAIM 2008

作者： Yu, Li School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China

ISBN: (纸本)9780769531854

Collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. It is not adapted to multi-interest or title recommendation for the 'general neighbourhood' problem which is analyzed in this paper. Based on it, collaborative filtering recommendation based on community is presented by introducing the concept 'community neighbourhood' in the paper. Unfortunately, it results into severer sparsity problem which makes heavy effect on its performance. In order to overcome it, an ontological A-priori score is used to infer user preference and to pre-fill null rating first. After pre-filling using the ontology method, then collaborative filtering based on community is executed based on a dense rating matrix. The experiment shows that collaborative filtering based on community makes generally better performance than traditional method when data is not very sparse, and ontology method can truly enhance collaborative filtering based on community since the sparsity is overcame. © 2008 IEEE.

关键词： Collaborative filtering

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COCA: More accurate multi-dimensional histograms out of more accurate correlations detection

COCA: More accurate multi-dimensional histograms out of more...

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9th International Conference on Web-Age Information Management, WAIM 2008

作者： Wei, Cao Xiongpai, Qin Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769531854

Detecting and exploiting correlations among columns in relational databases are of great value for query optimizers to generate better query execution plans (QEPs). We propose a more robust and informative metric, namely, entropy correlation coefficients, other than chi-square test to detect correlations among columns in large datasets. We introduce a novel yet simple kind of multi-dimensional synopses named COCA-Hist to cope with different correlations in databases. With the aid of the precise metric of entropy correlation coefficients, correlations of various degrees can be detected effectively;when correlation coefficients testify to mutual independence among columns, the AVI (attribute value independence) assumption can be adopted undoubtedly. COCA can also serve as a data-mining tool with superior qualities as CORDS does. We demonstrate the effectiveness and accuracy of our approach by several experiments. © 2008 IEEE.

关键词： Entropy

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Extracting multi-records from web pages

Extracting multi-records from web pages

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4th International Conference on Semantics, knowledge, and Grid, SKG 2008

作者： Tian, Xia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Resource Management Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769534015

Extracting multi-records from web pages is useful, it allows us to integrate information from multiple sources to provide value-added services. Existing techniques still have some limitations because of their several restrictions and accuracy. This paper proposes a new method to perform multi-records extraction task automatically. Firstly, the HTML tag tree is build based on an embedded browser interface to solve the AJAX problem. Secondly, data regions are found out by data chunk comparison, and simple tree matching method is proposed to compute the chunk similarity. Finally, the main data region is determined and the multi-records are extracted out. Experimental results show that our method dramatically outperforms other existing methods, and it can extract multi-records from pages very accurately. © 2008 IEEE.

关键词： Websites

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A Parallel Recovery Scheme for Update Intensive Main Memory database Systems

A Parallel Recovery Scheme for Update Intensive Main Memory ...

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IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Xiongpai Qin Yanqin Xiao Wei Cao Shan Wang Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing P.R. China

In update intensive applications, main memory database systems produce large volume of log records, it is critical to write out the log records efficiently to speedup transaction processing. We propose a parallel recovery scheme based on XOR differential logging for main memory database systems in such environments. Some NVRAM is used to temporarily hold log records and decouple transaction committing from disk writes, inherited parallelism properties of differential logging are exploited to accelerate log flushing by using multiple log disks. During recovery, log records are loaded from multiple log disks and applied to data partition in time without the need of reordering according to serialization order, total recovery time is cut down. The scheme employs a data partition based consistent checkpointing method. The log records are classified according to IDs of data partitions accessed. data partitions are recovered according to loading priorities computed from update frequencies and transaction waiting times, data access demands of new transactions coming after failure recovery are given attention immediately, thus the scheme provides system availability during recovery, which is of importance for large scale main memory database systems.

关键词： database systems data engineering Large-scale systems Random access memory Checkpointing Communication industry Defense industry Distributed computing Laboratories knowledge engineering

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The Research on the Algorithms of Keyword Search in Relational database

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2008年 4977卷 134-143页

作者： Li, Peng Zhu, Qing Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)354089375X

With the development of relational database, people require better database not only in the aspect of database performance, but also in the aspect of the database’s interactive ability. So that the database is much more friendly than just before and it is possible for a common user who do not have any special knowledge on database can access the database, without knowing the schema of database and writing intricate SQL. For the reason that the information retrieval on the web has developed well to some extent, when we develop the technology of keyword search in relational database, we can draw some ideas from information retrieve. But the great differences between the text database on the web and the relational database also bring some new challenges: 1) The answer needed by user is not only one tuple in database, but the tuple sets consisting of the tuple connect from different table using the "primary key-foreign key" relationship. 2)The results of the evaluation criteria is more important, because it is directly related to the effectiveness of Search System. 3)The structure of relational database is much more intricate than text database, and the algorithms of information retrieval are not fit the relational database. So in this paper, we introduce a novel keyword search algorithm and a modified criteria of evaluating answers in order to enhance efficiency of the keyword search and return much more effective information to users, finally, the search algorithm’s performance is tested and evaluated. © Springer-Verlag Berlin Heidelberg 2008

关键词： Information retrieval

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Greedy-Algorithm of KSORD Supporting Multi-language Phrase Recognition

Greedy-Algorithm of KSORD Supporting Multi-language Phrase R...

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International Conference on Advanced Computer Theory and engineering, ICACTE

作者： Peng Li Qing Zhu Chao Tian Shan Wang Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China

With the rapid development of information retrieval technology and daily increasing information in the Internet, common users can retrieve many text-based database and get part of the information through the search engines such as Google, and Baidu. However, there is a great amount of data contained in the background relational database of web pages. So there are many researches focusing on the search in these relational database with keywords, compared with these researches, our algorithms are mainly based on bags using the greedy algorithms and supporting the phrase recognition by utilizing multiple dictionaries. We make a comparison between our algorithm and the existing ones. The experiment results shows that our algorithm owns not only the feature of effectiveness but also the feature of efficiency.

关键词： Relational databases Internet Information retrieval data engineering knowledge engineering Search engines Dictionaries data structures Chaos Laboratories

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