Investigating a heterogeneous data integration approach for data warehousing

Data warehouses integrate data from remote, heterogeneous, autonomous data sources into a materialised central database. The heterogeneity of these data sources has two aspects, data expressed in different data models, called model heterogeneity, and data expressed within different schemas of the same data model, called schema heterogeneity. AutoMed is an approach to heterogeneous data transformation and integration based on the use of reversible schema transformation sequences, which offers the capability to handle data integration across heterogenous data sources. So far, this approach has been used only for virtual data integration. In this thesis, we investigate the use of this approach for materialised data integration. We investigate how AutoMed metadata can be used to express the schemas present in a data warehouse environment and to represent data warehouse processes such as data transformation, data cleansing, data integration, and data summarization. We discuss how the approach can be used for handling schema evolution in such a materialised data integration scenario. That is, if a data source or data warehouse schema evolves how the integrated metadata and data can also to be evolved so that the previous integration effort can be reused as much as possible. We then describe in detail how the approach can be used for two key data warehousing activities, namely data lineage tracing and incremental view See http://www.doc.ic.ac.uk/automed/