An Automatic Data Warehouse Conceptual Design Approach

Abstract

Within today’s competitive economic context, information acquisition, analysis and exploitation became strategic and unavoidable requirements for every enterprise. Moreover, in order to guarantee their persistence and growth, enterprises are forced, henceforth, to capitalize expertise in this domain. Data warehouses (DW) emerged as a potential solution answering the needs of storage and analysis of large data volumes. In fact, a DW is a database system specialized in the storage of data used for decisional ends. This type of systems was proposed to overcome the incapacities of OLTP (On-Line Transaction Processing) systems in offering analysis functionalities. It offers integrated, consolidated and temporal data to perform decisional analyses. However, the different objectives and functionalities between OLTP and DW systems created a need for a development method appropriate for DW. Indeed, data warehouses still deploy considerable efforts and interests of a large community of both software editors of decision support systems (DSS) and researchers (Kimball, 1996; Inmon, 2002). Current software tools for DW focus on meeting end-user needs. OLAP (On-Line Analytical Processing) tools are dedicated to multidimensional analyses and graphical visualization of results (e.g., Oracle Discoverer?); some products permit the description of DW and Data Mart (DM) schemes (e.g., Oracle Warehouse Builder?). One major limit of these tools is that the schemes must be built beforehand and, in most cases, manually. However, such a task can be tedious, error-prone and time-consuming, especially with heterogeneous data sources. On the other hand, the majority of research efforts focuses on particular aspects in DW development, cf., multidimensional modeling, physical design (materialized views (Moody & Kortnik, 2000), index selection (Golfarelli, Rizzi, & Saltarelli 2002), schema partitioning (Bellatreche & Boukhalfa, 2005)) and more recently applying data mining for a better data interpretation (Mikolaj, 2006; Zubcoff, Pardillo & Trujillo, 2007). While these practical issues determine the performance of a DW, other just as important, conceptual issues (e.g., requirements specification and DW schema design) still require further investigations. In fact, few propositions were put forward to assist in and/or to automate the design process of DW, cf., (Bonifati, Cattaneo, Ceri, Fuggetta & Paraboschi, 2001; Hahn, Sapia & Blaschka, 2000; Phipps & Davis 2002; Peralta, Marotta & Ruggia, 2003).