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 17th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of five papers, selected from the 24 full and 8 short papers presented at the 15th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2013, held in Prague, The Czech Republic, in August 2013. Of the five papers, two cover data warehousing aspects related to query processing optimization in advanced platforms, specifically Map Reduce and parallel databases, and three cover knowledge discovery, specifically the causal network inference problem, dimensionality reduction, and the quality-of-pattern-mining task.

Contains extended, revised versions of the best papers selected from DaWaK 2013 Both theory and real-world applications are covered Focuses on case studies issuing from international projects Includes supplementary material: sn.pub/extras

Autorentext
Roland Wagner, Kunsthistoriker und Germanist, ist Stipendiat der Fazit-Stiftung und Spezialist für den Einfluss der Philosophie Friedrich Nietzsches auf die Kunst.

Inhalt
Data Warehouse Processing Scale-Up for Massive Concurrent Queries with SPIN.- An Uncoupled Data Process and Transfer Model for Map Reduce.- Enhanced Fast Causal Network Inference over Event Streams.- Learning Through Non-linearly Supervised Dimensionality Reduction.- Metrics for Association Rule Clustering Assessment.