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Hameurlain, Abdelkader.

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Transactions on large-scale data- and knowledge-centered systems XXVIII = special issue on database- and expert-systems applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Transactions on large-scale data- and knowledge-centered systems XXVIII/ edited by Abdelkader Hameurlain ... [et al.].
Reminder of title:	special issue on database- and expert-systems applications /
remainder title:	Transactions on large-scale data- and knowledge-centered systems 28
other author:	Hameurlain, Abdelkader.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2016.,
Description:	xi, 157 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Databases. -
Online resource:	http://dx.doi.org/10.1007/978-3-662-53455-7
ISBN:	9783662534557$q(electronic bk.)

Transactions on large-scale data- and knowledge-centered systems XXVIII = special issue on database- and expert-systems applications /
Transactions on large-scale data- and knowledge-centered systems XXVIIIspecial issue on database- and expert-systems applications /[electronic resource] :Transactions on large-scale data- and knowledge-centered systems 28edited by Abdelkader Hameurlain ... [et al.]. - Berlin, Heidelberg :Springer Berlin Heidelberg :2016. - xi, 157 p. :ill., digital ;24 cm. - Lecture notes in computer science,99400302-9743 ;. - Lecture notes in computer science ;9940..

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.

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.

ISBN: 9783662534557$q(electronic bk.)

Standard No.: 10.1007/978-3-662-53455-7doiSubjects--Topical Terms:

747532
Databases.

LC Class. No.: QA76.9.D32

Dewey Class. No.: 005.74

Transactions on large-scale data- and knowledge-centered systems XXVIII = special issue on database- and expert-systems applications /
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505 0 $a 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.
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