Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII, 2013 Special Issue on Advances in Data Warehousing and Knowledge Discovery Transactions on Large-Scale Data- and Knowledge-Centered Systems Series

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 eighth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight revised selected regular papers focusing on the following topics: scalable data warehousing via MapReduce, extended OLAP multidimensional models, naive OLAP engines and their optimization, advanced data stream processing and mining, semi-supervised learning of data streams, incremental pattern mining over data streams, association rule mining over data streams, frequent pattern discovery over data streams.

ETLMR: A Highly Scalable Dimensional ETL Framework Based on MapReduce.- The Planning OLAP Model – A Multidimensional Model with Planning Support.- Query Optimization for the NOX OLAP Algebra.- Finding Critical Thresholds for Defining Bursts in Event Logs.- Concurrent Semi-supervised Learning with Active Learning of Data Streams.- Efficient Single Pass Ordered Incremental Pattern Mining.- Finding Interesting Rare Association Rules Using Rare Pattern Tree.- Discovering Frequent Patterns from Uncertain Data Streams with Time-Fading and Landmark Models.