Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV
- Special Issue on Data Management - Principles, Technologies, and Applications
- Indbinding:
- Paperback
- Sideantal:
- 195
- Udgivet:
- 10. september 2020
- Udgave:
- 12020
- Størrelse:
- 155x235x0 mm.
- Vægt:
- 454 g.
- 8-11 hverdage.
- 7. december 2024
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV
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 44th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six fully revised and extended papers selected from the 35th conference on Data Management ΓÇô Principles, Technologies and Applications, BDA 2019. The topics covered include big data, graph data streams, workflow execution in the cloud, privacy in crowdsourcing, secure distributed computing, machine learning, and data mining for recommendation systems.
This, the 44th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six fully revised and extended papers selected from the 35th conference on Data Management ΓÇô Principles, Technologies and Applications, BDA 2019. The topics covered include big data, graph data streams, workflow execution in the cloud, privacy in crowdsourcing, secure distributed computing, machine learning, and data mining for recommendation systems.
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