Transactions on Large-Scale Data- And Knowledge-Centered Systems VIII: Special Issue on Advances in Data Warehousing and Knowledge Discovery (Lecture Notes in Computer Science)

Abdelkader Hameurlain, Josef Kung, Roland Wagner

ISBN 10: 3642375731 / ISBN 13: 9783642375736

Published by Springer 2013-05-16, 2013

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Seller Inventory # 6666-IUK-9783642375736

Bibliographic Details

• Title: Transactions on Large-Scale Data- And Knowledge-Centered Systems VIII: Special Issue on Advances in Data Warehousing and Knowledge Discovery (Lecture Notes in Computer Science)
• Publisher: Springer 2013-05-16
• Publication Date: 2013
• Language: English
• ISBN 10: 3642375731
• ISBN 13: 9783642375736
• Binding: Paperback
• Condition: New

About this title

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 eighth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight revised selected regular papers focusing on the following topics: scalable data warehousing via MapReduce, extended OLAP multidimensional models, naive OLAP engines and their optimization, advanced data stream processing and mining, semi-supervised learning of data streams, incremental pattern mining over data streams, association rule mining over data streams, frequent pattern discovery over data streams.