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 volume, the 21st issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, focuses on Data Warehousing and Knowledge Discovery from Big Data, and contains extended and revised versions of eight papers selected as the best papers from the 14th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2012), held in Vienna, Austria, during September 3-6, 2012. These papers cover several advanced Big Data topics, ranging from data cube computation using MapReduce to multiple aggregations over multidimensional databases, from data warehousing systems over complex energy data to OLAP-based prediction models, from extended query engines for continuous stream analytics to popular pattern mining, and from rare pattern mining to enhanced knowledge discovery from large cross-document corpora.Selected Papers from DaWaK 2012 Abdelkader Hameurlain, Josef KA¼ng, Roland Wagner, Alfredo Cuzzocrea, Umeshwar Dayal ... Therefore, we use data related to the diploma delivery jury; the data grouping size on the dimension a#39;Coursesa#39; is five, i.e. each ... It uses (as in the classical model) a single aggregation function (a#39; AVG_Wa#39;), a The second query aggregates average marks at the semester level.
|Title||:||Transactions on Large-Scale Data- and Knowledge-Centered Systems XXI|
|Author||:||Abdelkader Hameurlain, Josef Küng, Roland Wagner, Alfredo Cuzzocrea, Umeshwar Dayal|
|Publisher||:||Springer - 2015-07-16|