Transactions on Large-Scale Data- and Knowledge-Centered Systems X: Special Issue on Database- and Expert-Systems Applications (Lecture Notes in Computer Science)

Abdelkader Hameurlain, Josef K�ng, Roland Wagner, Stephen W. Liddle, Klaus-Dieter Schewe, Xiaofang Zhou

ISBN 10: 3642412203 / ISBN 13: 9783642412202

Edité par Springer 2013-10-08, 2013

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N° de réf. du vendeur 6666-IUK-9783642412202

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Titre : Transactions on Large-Scale Data- and ...

Éditeur : Springer 2013-10-08

Date d'édition : 2013

Reliure : Paperback

Etat : New

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Synopsis :

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data-and knowledge-centered systems in large-scale environments. This, the 10th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains seven full papers chosen following two additional rounds of reviewing from revised and extended versions of a selection of papers presented at DEXA 2012. Topics covered include formal modelling and verification of web services, incremental computation of skyline queries, the implication problem for XML keys, lossless data compression, declarative view selection methods, time awareness in recommender systems, and network data mining.