An Ontological Approach to Handle Multidimensional Schema Evolution for Data Warehouse

In recent years, the number of digital information storage and retrieval systems has increased immensely. Data warehousing has been found to be an extremely useful technology for integrating such heterogeneous and autonomous information sources. Data within the data warehouse is modelled in the form of a star or snowflake schema which facilitates business analysis in a multidimensional perspective. As user requirements are interesting measures of business processes, the data warehouse schema is derived from the information sources and business requirements. Due to the changing business scenario, the information sources not only change their data, but also change their schema structure. In addition to the source changes the business requirements for data warehouse may also change. Both these changes results in data warehouse schema evolution. These changes can be handled either by just updating it in the DW model, or can be developed as a new version of the DW structure. Existing approaches either deal with source changes or requirements changes in a manual way and changes to the data warehouse schema is carried out at the physical level. This may induce high maintenance costs and complex OLAP server administration. As ontology seems to be a promising solution for the data warehouse research, in this paper an ontological approach to automate the evolution of a data warehouse schema is proposed. This method assists the data warehouse designer in handling evolution at the ontological level based on which decision can be made to carry out the changes at the physical level. We evaluate the proposed ontological approach with the existing method of manual adaptation of data warehouse schema.