UID:
almahu_9948017618002882
Format:
IX, 227 p. 86 illus., 71 illus. in color.
,
online resource.
ISBN:
9783662584156
Series Statement:
Transactions on Large-Scale Data- and Knowledge-Centered Systems, 11310
Content:
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 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.
Note:
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.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9783662584149
Additional Edition:
Printed edition: ISBN 9783662584163
Language:
English
DOI:
10.1007/978-3-662-58415-6
URL:
https://doi.org/10.1007/978-3-662-58415-6
URL:
Volltext
(lizenzpflichtig)
