Transactions on Large-Scale Data- and Knowledge-Centered Systems XVI -

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Résumé :
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 16th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of 7 papers, selected from the 30 papers presented at the International Conference on Advanced Computing and Applications, ACOMP 2013, held October 23-25, 2013, in Ho Chi Minh City, Vietnam. Topics covered include data engineering, information retrieval, query processing and optimization, energy-efficient resource allocation, and security and privacy.

Sommaire:
Visualizing Web Attack Scenarios in Space and Time Coordinate Systems.- Question-Answering for Agricultural Open Data.- Learning-Oriented Question Recommendation Using Bloom's Learning.- Taxonomy and Variable Length Hidden Markov Models.- On the Performance of Triangulation-Based Multiple Shooting Method for 2D Geometric Shortest Path Problems.- Protecting Biometric Features by Periodic Function-Based Transformation and Fuzzy Vault.- EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud.- Human Object Classification Using Dual Tree Complex Wavelet Transform and Zernike Moment.