Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not even resident within the company information system; their search, extraction, integration, and storage for reuse or sharing should be accomplished by users without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which data and metadata are associated with quality and provenance annotations.
A. Abelló, J. Darmont, L. Etcheverry, M. Golfarelli, J.N. Mazón, F. Naumann, et al. (2013). Fusion Cubes: Towards Self-Service Business Intelligence. INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING, 9(2), 1-23 [10.4018/jdwm.2013040104].
Fusion Cubes: Towards Self-Service Business Intelligence
GOLFARELLI, MATTEO;RIZZI, STEFANO;
2013
Abstract
Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not even resident within the company information system; their search, extraction, integration, and storage for reuse or sharing should be accomplished by users without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which data and metadata are associated with quality and provenance annotations.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.