Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII (eBook, PDF)

Special Issue on Database- and Expert-Systems Applications
Redaktion: Hameurlain, Abdelkader; Chen, Qimin; Wagner, Roland; Küng, Josef

• Format: PDF

Produktbeschreibung

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 28th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of six papers presented at the 26th International Conference on Database- and Expert-Systems Applications, DEXA 2015, held in Valencia, Spain, in September 2015. Topics covered include efficient graph processing, machine learning on big data, multistore big data integration, ontology matching, and the optimization of histograms for the Semantic Web.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 157
• Erscheinungstermin: 9. September 2016
• Englisch
• ISBN-13: 9783662534557
• Artikelnr.: 46927877

Inhaltsangabe

Accelerating Set Similarity Joins Using GPUs.- Divide-and-Conquer Parallelism for Learning Mixture Models.- Multistore Big Data Integration with CloudMdsQL.- Ontology Matching with Knowledge Rules.- Regularized Cost-Model Oblivious Database Tuning with Reinforcement Learning.- Workload-Aware Self-tuning Histograms for the Semantic Web.