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E-raamat: Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV: Special Issue on Data Management - Principles, Technologies, and Applications

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  • Formaat: EPUB+DRM
  • Sari: Lecture Notes in Computer Science 14160
  • Ilmumisaeg: 21-Sep-2023
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783662680148
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Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV: Special Issue on Data Management - Principles, Technologies, and ApplicationsSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Lecture Notes in Computer Science 14160
  • Ilmumisaeg: 21-Sep-2023
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783662680148
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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. 

This, the 54th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains three fully revised and extended papers and two additional extended keynotes selected from the 38th conference on Data Management - Principles, Technologies and Applications, BDA 2022. The topics cover a wide range of timely data management research topics on Temporal Graph Management, Tensor-based Data Mining, Time-Series Prediction, Healthcare Analytics over Knowledge Graphs, and Explanation of Database Query Answers.


Clock-G: Temporal graph management system.- TSPredIT: Integrated tuning
of data preprocessing and time series prediction models.- A guide to the
Tucker tensor decomposition for data mining: exploratory analysis, clustering
and classification.- Challenges for Healthcare Data Analytics over Knowledge
Graphs.- From Database Repairs to Causality in Databases and Beyond.
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