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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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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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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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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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data mining as a tool for environmental scientists

Data mining as a tool for environmental scientists

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3rd Biennial Meeting of the International Environmental Modelling and Software Society: Summit on Environmental Modelling and Software, iEMSs 2006

作者： Spate, Jessica Gibert, Karina Sànchez-Marrè, Miquel Frank, Eibe Comas, Joaquim Athanasiadis, Ioannis Letcher, Rebecca Mathematical Sciences Institute Australian National University Canberra Australia Department of Statistics and Operation Research Technical University of Catalonia Barcelona Catalonia Spain Knowledge Engineering and Machine Learning Group Technical University of Catalonia Barcelona Catalonia Spain Department of Computer Science University of Waikato Waikato New Zealand University of Girona Girona Catalonia Spain Istituto Dalle Molle di Studi Sull'Intelligenza Artificiale Lugano Switzerland Integrated Catchment Assessment and Management Centre Australian National University Canberra Australia

ISBN: (纸本)1424308526

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous.

关键词： data mining

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Semantics, Web and Mining: Preface

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) 2006年 4289 LNAI卷 v-vi页

作者： Berendt, Bettina Hotho, Andreas Mladenič, Dunja Semeraro, Giovanni Spiliopoulou, Myra Stumme, Gerd Van Someren, Maarten Ackermann, Markus Grobelnik, Marko Svátek, Vojtěch Institute of Information Systems Humboldt University Berlin Germany Knowledge and Data Engineering Group University of Kassel Germany J. Stefan Institute Ljubljana Slovenia Department of Informatics University of Bari Italy Faculty of Computer Science Otto-von-Guericke-Univ. Magdeburg Germany Informatics Institute University of Amsterdam Netherlands Dept. of Natural Language Processing Institute for Computer Science University of Leipzig Germany University of Economics Prague Czech Republic

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WISENT: e-science for Energy Meteorology

WISENT: e-Science for Energy Meteorology

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IEEE International Conference on e-science and Grid Computing

作者： Wilhelm Hasselbring Detlev Heinemann Johannes Hurka Thomas Scheidsteger Ludger Bischofs Christoph Mayer Jan Ploski Guido Scherp Sina Lohmann Carsten Hoyer-Klick Thilo Erbertseder Gerhard Gesell Marion Schroedter-Homscheidt Gerd Heilscher Jochen Rehwinkel Stefan Rensberg Software Engineering Group Department of Computer Science University of Oldenburg Oldenburg Germany EHF Laboratory Department of Physics University of Oldenburg Oldenburg Germany Business Information Management OFFIS-Institute for Computer Science Oldenburg Germany German AeroSpace Centre (DLR) Institute of Atmospheric Physics Wessling Germany German AeroSpace Centre (DLR) Institute of Technical Thermodynamics Stuttgart Germany German Remote Sensing Data Center (DFD) German Aerospace Center Wessling Germany Meteocontrol GmbH Augsburg Germany

ISBN: (纸本)9781424430116

Our energy production increasingly depends on renewable energy sources, which impose new challenges for distributed and decentralized systems. One problem is that the availability of renewable energy sources such as wind and solar is not continuous as it is affected by meteorological factors. The challenge is to develop forecast methods capable of determining the level of power generation in near real-time in order to control power plants for optimal energy production. Another scenario is the identification of optimal locations for such power plants. In our collaborative project, these tasks are investigated in the domain of energy meteorology. For that purpose large data sources from many different sensors (e.g., satellites and ground stations) are the base for complex computations. The idea is to parallelize these computations in order to obtain significant speedup. This paper reports on an ongoing project employing Grid technologies in that context. Our approach to processing large data sets from a variety of heterogeneous data sources as well as ideas for parallel and distributed computing in energy meteorology are presented. Preliminary experience with several Grid middleware systems in our application scenario is discussed.

关键词： Meteorology Renewable energy resources Power generation Production systems Meteorological factors Wind forecasting Weather forecasting Load forecasting Solar power generation Wind energy generation

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A Depth-first Algorithm of Finding All Association Rules Generated by a Frequent Itemset

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Journal of Donghua University(English Edition) 2006年第6期000卷

作者： WU Kun JIANG Bao-qing WEI Qing WU Kun(Institute of Data and Knowledge Engineering Henan University Kaifeng 475001 The Personnel Department Zhengzhou Institute of Aeronautical Industry ManagementZhengzhou 450000) JIANG Bao-qing(Institute of Data and Knowledge Engineering Henan University Kaifeng 475001) WEI Qing(The Computer Science Department Henan University of Finance and Economics Zhengzhou 450000)

The classical algorithm of finding association rules generated by a frequent itemset has to generate all nonempty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the *** show GRSET algorithm to be practicable and efficient.

关键词： association rule frequent itemset breath-first depth-first consequent

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