Designing multidimensional cubes from warehoused data and linked open data

TLDR: A new multidimensional model is provided, named Unified Cube, which offers a generic representation for both warehoused data and LOD at the conceptual level and is shown how analyses over data from different sources can be carried out through a Unified Cube.

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

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Frequently asked questions

Q1. What are the contributions in this paper?

In this paper, the authors presented a new approach to improve the quality of the data collected by the data collection system.