A Quality-Driven Approach for Building Heterogeneous Distributed Databases: The Case of Data Warehouses

Data Warehouse (DW) is a collection of data, consolidated from several heterogeneous sources, used to perform data analysis and support decision making in an organization. Extract-Transform-Load (ETL) phase plays a crucial role in designing DW. To overcome the complexity of the ETL phase, different studies have recently proposed the use of ontologies. Ontology-based ETL approaches have been used to reduce heterogeneity between data sources and ensure automation of the ETL process. Existing studies in semantic ETL have largely focused on fulfilling functional requirements. However, the ETL process quality dimension has not been sufficiently considered by these studies. As the amount of data has exploded with the advent of big data era, dealing with quality challenges in the early stages of designing the process become more important than ever. To address this issue, we propose to keep data quality requirements at the center of the ETL phase design. We present in this paper an approach, defining the ETL process at the ontological level. We define a set of quality indicators and quantitative measures that can anticipate data quality problems and identify causes of deficiencies. Our approach checks the quality of data before loading them into the target data warehouse to avoid the propagation of corrupted data. Finally, our proposal is validated through a case study, using Oracle Semantic DataBase sources (SDBs), where each source references the Lehigh University BenchMark ontology (LUBM).