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Transactions on Large-Scale Data- and Knowledge-Centered Systems LI: Special Issue on Data Management - Principles, Technologies and Applications 1st ed. 2022 [Pehme köide]

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  • Formaat: Paperback / softback, 137 pages, kõrgus x laius: 235x155 mm, kaal: 238 g, 44 Illustrations, color; 6 Illustrations, black and white; IX, 137 p. 50 illus., 44 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 13410
  • Ilmumisaeg: 08-Oct-2022
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662661101
  • ISBN-13: 9783662661109
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  • Formaat: Paperback / softback, 137 pages, kõrgus x laius: 235x155 mm, kaal: 238 g, 44 Illustrations, color; 6 Illustrations, black and white; IX, 137 p. 50 illus., 44 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 13410
  • Ilmumisaeg: 08-Oct-2022
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662661101
  • ISBN-13: 9783662661109
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 (e.g., computing resources, services, metadata, data sources) 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.

This, the 51st issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains five fully revised selected regular papers. Topics covered include data anonyomaly detection, schema generation, optimizing data coverage,  and digital preservation with synthetic DNA.

Threats Modeling And Anomaly Detection In The Behaviour Of A System - A
Review Of Some Approaches.- Incremental Schema Generation for Large and
Evolving RDF Sources.- Optimizing Data Coverage and Significance in Multiple
Hypothesis Testing on User Groups.- Efficiently identifying disguised missing
values in heterogeneous, text-rich data.- Digital Preservation with Synthetic
DNA.