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portada Transactions on Large-Scale Data- and Knowledge-Centered Systems ix (Lecture Notes in Computer Science, 7980)
Type
Physical Book
Publisher
Author
Language
English
Pages
133
Format
Paperback
ISBN13
9783642400681
Edition No.
2013th

Transactions on Large-Scale Data- and Knowledge-Centered Systems ix (Lecture Notes in Computer Science, 7980)

Hameurlain Abdelkader,Küng Josef,Wagner Roland (Author) · Springer · Paperback

Transactions on Large-Scale Data- and Knowledge-Centered Systems ix (Lecture Notes in Computer Science, 7980) - Hameurlain Abdelkader,Küng Josef,Wagner Roland

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Synopsis "Transactions on Large-Scale Data- and Knowledge-Centered Systems ix (Lecture Notes in Computer Science, 7980)"

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 ninth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers focusing on the following topics: top-k query processing in P2P systems, self-stabilizing consensus average algorithms in distributed sensor networks, recoverable encryption schemes, xml data in a multi-system environment, and pairwise similarity for cluster ensemble problems.

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