Transactions on Large-Scale Data- And Knowledge-Centered Systems XXII

ISBN-13: 9783662485668 / Angielski / Miękka / 2015 / 185 str.

Abdelkader Hameurlain; Josef Kung; Roland Wagner

Opis

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 22nd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six revised selected regular papers. Topics covered include algorithms for large-scale private analysis, modelling of entities from social and digital worlds and their relations, querying virtual security views of XML data, recommendation approaches using diversity-based clustering scores, hypothesis discovery, and data aggregation techniques in sensor network environments.

Spis treści

BPMiner: Algorithms for Large-scale Private Analysis.- System Modeling and Trust Evaluation of Distributed Systems.- Efficient Querying of XML Data through Arbitrary Security Views.- Increasing Coverage in Distributed Search and Recommendation with Profile Diversity.- Hypothesis Discovery Exploiting Closed Chains of Relations.- An Analysis of Variance-Based Methods for Data Aggregation in Periodic Sensor Networks.