Transactions on Large-Scale Data- and Knowledge-Centered Systems XIV

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 14th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains four revised selected regular papers. Topics covered include data stream systems, top-k query processing, semantic web service (SWS) discovery, and XML functional dependencies.

Contains in-depth research on data and knowledge management systems Topics covered range from data stream systems to data integration and metadata management Topics of query processing and optimization, and Web services, are thoroughly explored Includes supplementary material: sn.pub/extras

Autorentext
Roland Wagner, Kunsthistoriker und Germanist, ist Stipendiat der Fazit-Stiftung und Spezialist für den Einfluss der Philosophie Friedrich Nietzsches auf die Kunst.

Inhalt
Reliable Aggregation over Prioritized Data Streams.- Slicing the Dimensionality: Top-k Query Processing for High-Dimensional Spaces.- SeeVa: A Model Based Framework for Semantic Web Service Discovery.- Maximal Set of XML Functional Dependencies for the Integration of Multiple Systems.