Transactions on Large-Scale Data- And Knowledge-Centered Systems XXVI: Special Issue on Data Warehousing and Knowledge Discovery

ISBN-13: 9783662497838 / Angielski / Miękka / 2016 / 109 str.

Abdelkader Hameurlain; Josef Kung; Roland Wagner

Opis

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 26th 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 four papers selected as the best papers from the 16th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2014), held in Munich, Germany, during September 1-5, 2014. The papers focus on data cube computation, the construction and analysis of a data warehouse in the context of cancer epidemiology, pattern mining algorithms, and frequent item-set border approximation.

Spis treści

Banded Pattern Mining Algorithms in Multi-dimensional Zero-One Data.- Frequent Item-set Border Approximation by Dualization.- Dynamic Materialization for Building Personalized Smart Cubes.- Opening up Data Analysis for Medical Health Services: Data Integration and Analysis in Cancer Registries with CARESS.