Designing Multidimensional Cubes from Warehoused Data and Linked Open Data

Ravat, Franck, Song, Jiefu and Teste, Olivier (2016) Designing Multidimensional Cubes from Warehoused Data and Linked Open Data. In: 10th International IEEE Conference on Research Challenges in Information Science (RCIS 2016) co-located witht the 34th French Conference INFORSID.

[img]
Preview
Text
Download (2MB) | Preview

Abstract

A Data Warehouse (DW) is widely used as a consistent and integrated data repository in Business Intelligence systems. Under today's dynamic and competitive business context, warehoused data alone no longer provide enough information for decision-making processes. Business analyses should be enhanced by including Linked Open Data (LOD) to offer multiple perspectives to decision-makers. This paper provides a new multidimensional model, named Unified Cube, which offers a generic representation for both warehoused data and LOD at the conceptual level. A two-stage process is proposed to build a Unified Cube according to decision-makers' needs. As a first step, schemas published with specific modeling languages are transformed into a common conceptual representation. The second step is to associate together related data to form a Unified Cube containing all useful information about an analysis subject. A high-level declarative language is provided to enable nonexpert users to define the relevance between data according to their analysis needs. To demonstrate the feasibility of the proposed concepts, we show how analyses over data from different sources can be carried out through a Unified Cube.

Item Type: Conference or Workshop Item (Paper)
Language: English
Date: 2016
Uncontrolled Keywords: Conceptual multidimensional modeling - Linked open data - Unified business analyses
Subjects: H- INFORMATIQUE
Divisions: Institut de Recherche en Informatique de Toulouse
Site: UT1
Date Deposited: 26 Mar 2019 13:04
Last Modified: 26 Mar 2019 13:04
URI: http://publications.ut-capitole.fr/id/eprint/28816

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year