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International Conference on Peer-to-Peer Computing

作者： Cong Shi Shicong Meng Yuanjie Liu Dingyi Han Yong Yu Apex Data and Knowledge Management Laboratory Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

The idea of building query-oriented routing indices has changed the way of improving routing efficiency from the basis as it can learn the content distribution during the query routing process. It gradually improves routing efficiency with no excessive network overhead of the routing index construction and maintenance. However, the previously proposed mechanism is not practically effective due to the slow improvement of routing efficiency. In this paper, we propose a novel mechanism for query-oriented routing indices which quickly achieves high routing efficiency at low cost. The maintenance method employs reinforcement learning to utilize mass peer behaviors to construct and maintain routing indices. It explicitly uses the expected value of returned content number to depict the content distribution, which helps quickly approximate the real distribution. Meanwhile, the routing method is to retrieve as many contents as possible. It also helps speed up the learning process further. The experimental evaluation shows that the mechanism has high routing efficiency, quick learning ability and satisfactory performance under churn

关键词： Learning Routing Peer to peer computing Query processing Buildings knowledge management Computer science data engineering knowledge engineering Costs

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Mining and Predicting Duplication over Peer-to-Peer Query Streams

Mining and Predicting Duplication over Peer-to-Peer Query St...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Shicong Meng Yifeng Shao Cong Shi Dingyi Han Yong Yu APEX Data and Knowledge Management Laboratory Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

Many previous works of data mining user queries in peer-to-peer systems focused their attention on the distribution of query contents. However, few has been done towards a better understanding of the time series distribution of these queries, which is vital for system performance. To remedy this situation, this paper mines query steams by using automatic time series analysis to evaluate different linear models (Box-Jenkins models and some simple windowed-mean models) for predicting the number of duplicated queries from 10 minutes to 2 hours into the future. Both the predictive power and the computational costs of these models are evaluated over 318,942,450 real world Gnutella queries collected over 3 months. We find the number of duplicated queries is consistently predictable. Simple, practical models like AR perform well on prediction

关键词： Peer to peer computing Predictive models Power system modeling data mining System performance Crawlers Time series analysis Computational efficiency Load management knowledge management

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Cuckoo Ring: BalancingWorkload for Locality Sensitive Hash

Cuckoo Ring: BalancingWorkload for Locality Sensitive Hash

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International Conference on Peer-to-Peer Computing

作者： Dingyi Han Ting Shen Shicong Meng Yong Yu Apex Data & Knowledge Management Laboratory Department of Computer Science & Engineering Shanghai Jiaotong University Shanghai China

Locality sensitive hash (LSH) is widely used in peer-to-peer (P2P) systems. Although it can support range or similarity queries, it breaks the load balance mechanism of traditional distributed hash table (DHT) based system by replacing consistent hash with LSH. To solve the imbalance problem, current systems either weaken the locality preserve ability from similarity preserved to order preserved or adopt load aware peer join mechanism. The first method does not support similarity query as it loses the similarity information and the second method is greatly affected by the dynamic nature of P2P networks. In this paper, we propose a novel system, cuckoo ring, which can preserve similarity information while load balanced. It does not guide the newly joining peer to the hot areas but move the items in the hot areas to cold areas so that the short life time peers are distributed uniformly across the network instead of being guided to the hot areas. Compared to traditional DHT systems, cuckoo ring only maintains a little more information about the global light load peers and the moved indexed items

关键词： Indexing Load management knowledge management Computer science data engineering knowledge engineering Nearest neighbor searches Q measurement

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Information retrieval in folksonomies: Search and ranking

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3rd European Semantic Web Conference, ESWC 2006

作者： Hotho, Andreas Jäschke, Robert Schmilz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D-34121 Kassel Germany Research Center L3S Expo Plaza 1 D-30539 Hannover Germany

ISBN: (纸本)3540345442

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Information retrieval systems

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Emergent semantics in BibSonomy

Emergent semantics in BibSonomy

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36th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Informatik fur Menschen, INFORMATIK 2006

作者： Hotho, Andreas Jäschke, Robert Schmitz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D-34121 Kassel Germany Research Center L3S Expo Plaza 1 D-30539 Hannover Germany

ISBN: (纸本)9783885791881

关键词： Semantics

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Runtime Semantic Interoperability for Gathering Ontology-based Network Context

Runtime Semantic Interoperability for Gathering Ontology-bas...

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IEEE Symposium on Network Operations and Management

作者： J. Keeney D. Lewis D. O'Sullivan A. Roelens V. Wade A. Boran R. Richardson Knowledge & Data Engineering Group Department of Computer Science Trinity College Dublin Dublin Ireland Bell Laboratories Ireland

The trends for pushing more operational intelligence towards network elements to achieve more context-aware and self-managing behavior often requires elements to gather network knowledge without necessarily binding explicitly to all of the potential sources of that knowledge. Though event-based publish-subscribe models allow efficient distribution of knowledge where the event types are known globally, dynamic service chains, ad hoc networks and pervasive computing application all introduce a more fluid and heterogeneous range of context knowledge. This requires some runtime translation of knowledge between sources and sinks of network context. This paper builds on existing mapping techniques that use ontological forms of existing management information models to examine the extent to which these can be employed for runtime semantic interoperability for network knowledge. It presents results in developing a management knowledge delivery framework based on existing models and platforms, but which offers a more decentralized knowledge exchange mechanism

关键词： Runtime Ontologies knowledge management Intelligent networks Publish-subscribe Context modeling Fluid dynamics Ad hoc networks Pervasive computing Context-aware services

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Mining Maximal Frequent Patterns in a Unidirectional FP-tree

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Journal of Donghua University(English Edition) 2006年第6期000卷 105-109页

作者： SONG Jing-jing LIU Rui-xin WANG Yan JIANG Bao-qing SONG Jing-jing(Institute of Data and Knowledge Engineering Henan University Kaifeng 475001) LIU Rui-xin(Information Engineering Science Department Yellow River Conservancy Technical Institute Kaifeng 475003) WANG Yan(Institute of Data and Knowledge Engineering Henan University Kaifeng 475001 Department of Computer Science and Application Zhengzhou Institute of Aeronautical Industry Management Zhengzhou 450015)

Becausemining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However,because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques:single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_ FP-Max further lowers the expense of time and space.

关键词： data mining frequent pattern the maximal frequent pattern Unid _ FP-tree conditional Unid _ FP-tree

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Folkrank: A ranking algorithm for folksonomies

Folkrank: A ranking algorithm for folksonomies

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Workshop-Woche on Lernen, Wissen und Adaptivitat, LWA 2006

作者： Hotho, Andreas Jäschke, Robert Schmitz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D34121 Kassel Germany Research Center L3S Expo Plaza 1 D30539 Hannover Germany

In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006 [3].

关键词： Semantic Web

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Recent trends in data Mining (DM): Document Clustering of DM Publications

Recent trends in Data Mining (DM): Document Clustering of DM...

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International Conference on Services Systems and Services Management, ICSSSM

作者： Yi Peng Gang Kou Zhengxin Chen Yong Shi Peter Kiewit Institute ofInformation Science Technology & Engineering University of Nebraska Omaha NE USA Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Chinese Academy of Sciences Beijing China

data mining (DM) brings knowledge and theories from several fields including databases, machine learning, optimization, statistics, and data visualization and has been applied to various real-life applications. A large amount of data mining articles have been published. The goal of this study is to establish an overview of the past and current data mining research activities from the title and abstract more than 1400 textual documents collected from premier data mining journals and conference proceedings. Specifically, this study applied document clustering approaches to determine which subjects had been studied over the last several years, which subjects are currently popular, and describe the longitudinal changes of data mining publications

关键词： Delta modulation data mining Machine learning Conference proceedings IEL data engineering Visual databases Statistics data visualization Software libraries

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Application of Clustering Methods to Health Insurance Fraud Detection

Application of Clustering Methods to Health Insurance Fraud ...

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International Conference on Services Systems and Services Management, ICSSSM

作者： Yi Peng Gang Kou Alan Sabatka Zhengxin Chen Deepak Khazanchi Yong Shi Peter Kiewit Institute of Information Science Technology & Engineering University of Nebraska Omaha Omaha NE USA Mutual of Omaha Omaha NE USA Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Chinese Academy of Sciences Beijing China

Health insurance fraud detection is an important and challenging task. Traditionally, insurance companies use human inspections and heuristic rules to detect fraud. As the size of databases increases, the traditional approaches may miss a great portion of fraud for two main reasons. First, it is impossible to detect all health care fraud by manual inspection over large databases. Second, new types of health care fraud emerge constantly. SQL operations based on heuristic rules cannot identify those new emerging fraud schemes. Such a situation demands more sophisticated analytical methods and techniques that are capable of detecting fraud activities from large databases. The goal of this paper is to understand and detect suspicious health care frauds from large databases using clustering technique. Specifically, this paper applies two clustering methods, SAS EM and CLUTO, to a large real-life health insurance dataset and compares the performances of these two methods

关键词： Clustering methods Insurance Medical services Inspection Synthetic aperture sonar Humans data mining Spatial databases Cities and towns Input variables

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