Ontology-driven Conceptual Design of ETL Processes using Graph Transformations

Dimitrios Skoutas, Alkis Simitsis, Timos Sellis
LNCS Journal on Data Semantics (JoDS), Special issue on
Abstract. One of the main tasks during the early steps of a data warehouse project is the identification of the appropriate transformations and the specification of inter-schema mappings from the source to the target data stores. This is a challenging task, requiring firstly the semantic and secondly the structural reconciliation of the information provided by the available sources. This task is a part of the Extract-Transform-Load (ETL) process, which is responsible for the population of the data warehouse. In this paper, we propose a customizable and extensible ontology-driven approach for the conceptual design of ETL processes. A graph-based representation is used as a conceptual model for the source and target data stores. We then present a method for devising flows of ETL operations by means of graph transformations. In particular, the operations comprising the ETL process are derived through graph transformation rules, the choice and applicability of which are determined by the semantics of the data with respect to an attached domain ontology. Finally, we present our experimental findings that demonstrate the applicability of our approach.