Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVII (eBook, PDF)

Redaktion: Hameurlain, Abdelkader; Wagner, Roland

• Format: PDF

Produktbeschreibung

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,the 37th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers. Topics covered include data security in clouds, privacy languages, probabilistic modeling in linked data integration, business intelligence based on multi-agent systems, and collaborative filtering and prediction accuracy.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 193
• Erscheinungstermin: 1. August 2018
• Englisch
• ISBN-13: 9783662579329
• Artikelnr.: 53475164

Inhaltsangabe

Keeping Secrets by Separation of Duties while Minimizing the Amount of Cloud Servers.- LPL, Towards a GDPR-Compliant Privacy Language: Formal Definition and Usage.- Quantifying and Propagating Uncertainty in Automated Linked Data Integration.- A Comprehensive Approach for Designing Business-Intelligence Solutions with Multi-Agent Systems in Distributed Environments.- Enhancing Rating Prediction Quality through Improving the Accuracy of Detection of Shifts in Rating Practices.