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Record Nr. |
UNINA9910427716803321 |
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Titolo |
Transactions on large-scale data- and knowledge-centered systems XLIV : special issue on data management - principles, technologies, and applications / / Abdelkader Hameurlain [and three others], (Eds.) |
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Pubbl/distr/stampa |
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Berlin, Germany : , : Springer, , [2020] |
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©2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (VII, 195 p. 75 illus., 44 illus. in color.) |
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Collana |
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Lecture Notes in Computer Science ; ; 12380 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Scalable Saturation of Streaming RDF Triples -- Efficient Execution of Scientific Workflows in the Cloud through Adaptive Caching -- From Task Tuning to Task Assignment in Privacy-Preserving -- Secure Distributed Queries over Large Sets of Personal Home Boxes -- Evaluating Classification Feasibility Using Functional Dependencies -- Enabling Decision Support through Ranking and Summarization of Association Rules for TOTAL Customers. |
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Sommario/riassunto |
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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 (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 - Principles, Technologies and Applications, BDA 2019. The topics covered include big data, graph data streams, workflow execution in the cloud, privacy |
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in crowdsourcing, secure distributed computing, machine learning, and data mining for recommendation systems. |
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