LOD for Data Warehouses: Managing the Ecosystem Co-Evolution

Abstract

For more than 30 years, data warehouses (DWs) have attracted particular interest both in practice and in research. This success is explained by their ability to adapt to their evolving environment. One of the last challenges for DWs is their ability to open their frontiers to external data sources in addition to internal sources. The development of linked open data (LOD) as external sources is an excellent opportunity to create added value and enrich the analytical capabilities of DWs. However, the incorporation of LOD in the DW must be accompanied by careful management. In this paper, we are interested in managing the evolution of DW systems integrating internal and external LOD datasets. The particularity of LOD is that they contribute to evolving the DW at several levels: (i) source level, (ii) DW schema level, and (iii) DW design-cycle constructs. In this context, we have to ensure this co-evolution, as conventional evolution approaches are adapted neither to this new kind of source nor to semantic constructs underlying LOD sources. One way of tackling this co-evolution issue is to ensure the traceability of DW constructs for the whole design cycle. Our approach is tested using: the LUBM (Lehigh University BenchMark), different LOD datasets (DBepedia, YAGO, etc.), and Oracle 12c database management system (DBMS) used for the DW deployment.