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International Conference on Advanced Information Networking and Applications (AINA)

作者： S.M. Deen K. Ponnamperuma Data and Knowledge Engineering DAKE Group Department of Computer Science University of Keele UK

We present here an approach for dynamic ontology integration for a multi-agent environment, in which each agent holds the ontologies of its acquaintances (i.e., other agents of its interest) as the integrated partial global ontology, which is essential to interpret the local schemas for inter-agent operations. This integration has to be carried out whenever a new acquaintance is added or when the local ontology of an acquaintance changes. The approach described is general (i.e., independent of any particular thesaurus) and it carries out the integration automatically except for minimal unavoidable human inputs to resolve semantic conflicts if discovered in the process.

关键词： Ontologies Thesauri Humans Semantic Web Peer to peer computing Multiagent systems knowledge engineering Computer science Shape Scalability

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An Economic Semantic Web Platform

An Economic Semantic Web Platform

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International Conference on Grid and Cooperative Computing Workshops (GCCW)

作者： Xiaoyong Du He Hu Man Li Wenjuan Wu Guo Qin Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing China

The paper describes an ongoing project which implements a subject-oriented semantic Web platform at Renmin Univ. of China. The economic semantic Web platform (ESWP) contains three components: collaborative ontology developing environment and repository system (CODERS); economic ontology annotation Web services (ConAnnotator); economic ontology and annotated resources. We describe each of these components in detail and illustrate some use cases of the ESWP

关键词： Semantic Web Ontologies Environmental economics Web services Collaboration Humans Software agents Application software Helium Laboratories

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Density Estimation Technique for data Stream Classification

Density Estimation Technique for Data Stream Classification

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International Workshop on database and Expert Systems Applications

作者： N. Kerdprasop K. Kerdprasop Data Engineering and Knowledge Discovery Research Unit School of Computer Engineering Suranaree University슠of Technology Thailand

Density estimation is an important pre-processing step in the problem of data stream classification in which the number of data is overwhelming and the exact data distribution is unknown. We simplify the problem by employing a statistical sampling technique to obtain an approximate solution. With the proposed method, an unbounded large data set can be sampled in a number of random configurations, and that data can be used to describe the data set as a whole. The efficiency of the method depends largely on the ability to draw samples effectively which in turn depends on how close we can estimate the target density. We use finite mixture models to represent the probability density functions of the data stream. Then, we apply the EM algorithm twice to learn the model parameters. The efficiency of our estimation technique has been shown in the experimental results

关键词： Sampling methods data mining data engineering Probability density function data analysis Algorithm design and analysis Performance analysis Parameter estimation Distributed computing knowledge engineering

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A Framework for the Decentralisation and Management of Collaborative Applications in Ubiquitous Computing Environments

A Framework for the Decentralisation and Management of Colla...

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IEEE Symposium on Network Operations and Management

作者： K. Quinn A. Kenny K. Feeney D. Lewis D. O'Sullivan V.P. Wade Knowledge & Data Engineering Group Department of Computer Science Trinity College Dublin Dublin Ireland

When deploying collaborative applications such as instant messaging in ubiquitous computing environments significant enhancements can be afforded by offering additional context information, such as location information. However, such environments exert key challenges such as increased diversity of ownership and ad hoc, intermittent network connectivity that suits more decentralized computing architectures. This paper examines how a migration to a more decentralized collaborative architecture can be achieved together with a decentralization of the management of collaborative activities

关键词： Environmental management Collaboration Ubiquitous computing Access control Application software Computer architecture Routing Humans Content management Peer to peer computing

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TRIAS--An Algorithm for Mining Iceberg Tri-Lattices

TRIAS--An Algorithm for Mining Iceberg Tri-Lattices

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IEEE International Conference on data Mining (ICDM)

作者： Robert Jaschke Andreas Hotho Christoph Schmitz Bernhard Ganter Gerd Stumme Knowledge & Data Engineering Group University of Kassel Germany Institute for Algebra Dresden University of Technology Germany

In this paper, we present the foundations for mining frequent tri-concepts, which extend the notion of closed item-sets to three-dimensional data to allow for mining folk-sonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution as well as experimental results on a large real-world example.

关键词： data mining Itemsets Resource management Algorithm design and analysis Association rules data structures data engineering Algebra Lattices Clustering algorithms

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Benchmarking knowledge-based Context Delivery Systems

Benchmarking Knowledge-based Context Delivery Systems

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International Conference on Autonomic and Autonomous Systems (ICAS)

作者： J. Keeney D. Lewis D. O'Sullivan Knowledge & Data Engineering Group and CTVR Department of Computer Science Trinity College Dublin Dublin Ireland

As context-aware systems become more widespread and mobile there is an increasing need for a common distributed event platform for gathering context information and delivering to context-aware applications. The likely heterogeneity across the body of context information can be addressed using runtime reasoning over ontology-based context models. However, existing knowledge-based reasoning is not typically optimised for real-time operation so its inclusion in any context delivery platform needs to be carefully evaluated from a performance perspective. In this paper we propose a benchmark for knowledge-based context delivery platforms and in particular examine suitable knowledge benchmarks for assessing the ability of platforms to deal with semantic interoperability

关键词： Context-aware services Ontologies Pervasive computing Context modeling Temperature sensors Acoustic sensors knowledge management Publish-subscribe data engineering Computer science

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Algebraic Independence Measures for the Values of Mahler Type Functions

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Chinese Quarterly Journal of Mathematics 2006年第4期21卷 567-576页

作者： WANG Tian-qin LIU Hua-ke Institute of Data and Knowledge Engineering Henan University Kaifeng 475001 China College of Mathematics and Information Science Henan University Kaifeng 475001 China

In this paper, we give the algebraic independence measures for the values ofMahler type functions in complex number field and p-adic number field, respectively.

关键词： algebraic independence measure Mahler type function p-adic number field

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Reinforcement Learning for Query-Oriented Routing Indices in Unstructured Peer-to-Peer Networks

Reinforcement Learning for Query-Oriented Routing Indices in...

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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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Towards the knowledge-driven benchmarking of autonomic communications

Towards the knowledge-driven benchmarking of autonomic commu...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： D. Lewis D. O' Sullivan J. Keeney Knowledge and Data Engineering Group Trinity College Dublin Ireland University of Dublin Trinity College Dublin IE

Currently a wide range of different adaptive and intelligent system solutions are being proposed for use in self-managing or autonomic networks. However, there are few means by which such proposals can be compared. This paper proposes that a benchmark be developed for autonomic systems so that progress in this field can be more systematically evaluated. Our approach assumes that autonomic systems make use of and thus expose a knowledge based representation of the service they offer, the context they react to and the governance to which they are subject. This position paper focuses on some of the issues that arise when formulating a benchmark for autonomic communications and is intended to form the basis for further discussion in the area

关键词： Humans Costs Control systems Context Pervasive computing Monitoring Artificial intelligence data engineering Educational institutions Adaptive systems

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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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