Atnaujinkite slapukų nuostatas

Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII: Special Issue on Database- and Expert-Systems Applications 1st ed. 2016 [Minkštas viršelis]

Edited by , Edited by , Edited by , Edited by
  • Formatas: Paperback / softback, 157 pages, aukštis x plotis: 235x155 mm, weight: 2701 g, 43 Illustrations, black and white; XI, 157 p. 43 illus., 1 Paperback / softback
  • Serija: Lecture Notes in Computer Science 9940
  • Išleidimo metai: 19-Sep-2016
  • Leidėjas: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662534541
  • ISBN-13: 9783662534540
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 157 pages, aukštis x plotis: 235x155 mm, weight: 2701 g, 43 Illustrations, black and white; XI, 157 p. 43 illus., 1 Paperback / softback
  • Serija: Lecture Notes in Computer Science 9940
  • Išleidimo metai: 19-Sep-2016
  • Leidėjas: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662534541
  • ISBN-13: 9783662534540
Kitos knygos pagal šią temą:
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.

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.

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.