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Transactions on Large-Scale ­Data- and Knowledge-Centered­ Systems XXXIX
Special Issue on Database- and Expert-Systems Applications (Lecture Notes in Computer Science)
By Abdelkader Hameurlain (Edited by), Roland Wagner (Edited by), Djamal Benslimane (Edited by), Ernesto Damiani (Edited by)

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Format
Paperback, 227 pages
Published
Germany, 1 December 2018

This, the 39th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers selected from the 37 contributions presented at the 28th International Conference on Database and Expert Systems Applications, DEXA 2017, held in Lyon, France, in August 2017. Topics covered include knowledge bases, clustering algorithms, parallel frequent itemset mining, model-driven engineering, virtual machines, recommendation systems, and federated SPARQL query processing.


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Product Description

This, the 39th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers selected from the 37 contributions presented at the 28th International Conference on Database and Expert Systems Applications, DEXA 2017, held in Lyon, France, in August 2017. Topics covered include knowledge bases, clustering algorithms, parallel frequent itemset mining, model-driven engineering, virtual machines, recommendation systems, and federated SPARQL query processing.

Product Details
EAN
9783662584149
ISBN
366258414X
Other Information
71 Illustrations, color; 15 Illustrations, black and white; IX, 227 p. 86 illus., 71 illus. in color.
Dimensions
23.4 x 15.6 x 1.3 centimetres (0.45 kg)

Table of Contents

Querying Interlinked Data by Bridging RDF Molecule Templates.- A Package-to-Group Recommendation Framework.- Statistical Relation Cardinality Bounds in Knowledge Bases.- ETL Environment in the Era of Variety.- eVM: An Event Virtual Machine Framework.- Interactive Exploration of Subspace Clusters on Multicore Processors.- MapFIM+: Memory Aware Parallelized Frequent Itemset Mining in Very Large Datasets.

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