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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVI: Special Issue on Data Warehousing and Knowledge Discovery: Lecture Notes in Computer Science, cartea 9670

Editat de Abdelkader Hameurlain, Josef Küng, Roland Wagner, Ladjel Bellatreche, Mukesh Mohania
en Limba Engleză Paperback – 29 mar 2016
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. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This volume, the 26th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, focuses on Data Warehousing and Knowledge Discovery from Big Data, and contains extended and revised versions of four papers selected as the best papers from the 16th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2014), held in Munich, Germany, during September 1-5, 2014. The papers focus on data cube computation, the construction and analysis of a data warehouse in the context of cancer epidemiology, pattern mining algorithms, and frequent item-set border approximation.
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Specificații

ISBN-13: 9783662497838
ISBN-10: 3662497832
Pagini: 109
Ilustrații: XI, 109 p. 43 illus.
Dimensiuni: 155 x 235 x 7 mm
Greutate: 0.19 kg
Ediția:1st ed. 2016
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Transactions on Large-Scale Data- and Knowledge-Centered Systems

Locul publicării:Berlin, Heidelberg, Germany

Cuprins

Banded Pattern Mining Algorithms in Multi-dimensionalZero-One Data.- Frequent Item-set Border Approximation by Dualization.- DynamicMaterialization for Building Personalized Smart Cubes.- Opening up DataAnalysis for Medical Health Services: Data Integration and Analysis in CancerRegistries with CARESS.

Textul de pe ultima copertă

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. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments.
Thisvolume, the 26th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, focuses on Data Warehousing and Knowledge Discovery from Big Data, and contains extended and revised versions of four papers selected as the best papers from the 16th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2014), held in Munich, Germany, during September 1-5, 2014. The papers focus on data cube computation, the construction and analysis of a data warehouse in the context of cancer epidemiology, pattern mining algorithms, and frequent item-set border approximation.

Caracteristici

Contains extended and revised version of the best papers selected from DaWaK 2014 Papers focus on aspects of data warehousing and knowledge discovery Topics range from data cube computation to pattern-mining algorithms Includes supplementary material: sn.pub/extras