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 10th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains seven full papers chosen following two additional rounds of reviewing from revised and extended versions of a selection of papers presented at DEXA 2012. Topics covered include formal modelling and verification of web services, incremental computation of skyline queries, the implication problem for XML keys, lossless data compression, declarative view selection methods, time awareness in recommender systems, and network data mining.
Table of ContentsStepwise Development of Formal Models for Web Services Compositions: Modelling and Property Verification.- Computing Skyline Incrementally in Response to Online Preference Modification.- The Finite Implication Problem for Expressive XML Keys: Foundations, Applications, and Performance Evaluation.- ALACRITY: Analytics-Driven Lossless Data Compression for Rapid In-Situ Indexing, Storing, and Querying.- A Declarative Approach to View Selection Modeling.- A Framework for Modeling, Computing and Presenting Time-Aware Recommendations.- Incremental Mining of Top-k Maximal Influential Paths in Network Data.