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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII: Special Issue on Database- and Expert-Systems Applications 2018 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 173 pages, height x width: 235x155 mm, weight: 454 g, 10 Illustrations, color; 72 Illustrations, black and white; XI, 173 p. 82 illus., 10 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 11250
  • Izdošanas datums: 05-Dec-2018
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662583836
  • ISBN-13: 9783662583838
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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII: Special Issue on Database- and Expert-Systems Applications 2018 ed.Palielināt
  • Formāts: Paperback / softback, 173 pages, height x width: 235x155 mm, weight: 454 g, 10 Illustrations, color; 72 Illustrations, black and white; XI, 173 p. 82 illus., 10 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 11250
  • Izdošanas datums: 05-Dec-2018
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662583836
  • ISBN-13: 9783662583838
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783662583838.html
Keywords:
This, the 38th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of six papers selected from the 68 contributions presented at the 27th International Conference on Database and Expert Systems Applications, DEXA 2016, held in Porto, Portugal, in September 2016. Topics covered include query personalization in databases, data anonymization, similarity search, computational methods for entity resolution, array-based computations in big data analysis, and pattern mining.

Bound-and-Filter Framework for Aggregate Reverse Rank Queries.- Syntactic Anonymisation of Shared Datasets in Resource Constrained Environments.- Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries.- SjClust: A Framework for Incorporating Clustering into Set Similarity Join Algorithms.- A Query Processing Framework for Large-Scale Scientific Data Analysis.- Discovering Periodic-Correlated Patterns in Temporal Databases.