Title: Data Integration for Multidimensional Data Models.

Abstract
Consider a scenario where, as a result of company acquisitions and mergers, a number of related, but possible disparate, data marts need to be integrated into a global data warehouse. The ability to retrieve data across these disparate, but related, data marts posed an important challenge. For example, forming an all-inclusive data warehouse includes the tedious tasks of identifying related fact and dimension table attributes, as required for a schema merge. Additionally, the evaluation of the combined set of correct answers to queries, likely to be independently posed to such data marts, becomes difficult to achieve.

Model management refers to a high-level, abstract programming language designed to efficiently manipulate schemas and mappings, with applications in meta-data management, e-Commerce and data integration, amongst others. Particularly, model and meta-data management operations, in the form of Match and Merge algorithms, offer a way to address the above-mentioned data integration and schema matching issues within the data warehousing domain.

In this presentation, we introduce an approach for the integration of source data marts into a global data warehouse. We discuss the development of three (3) streamlined steps to facilitate the generation of a global data warehouse. That is, we present techniques for deriving attribute correspondences, for schema mapping discovery, as well as a merge algorithm, within the context of multidimensional star schemas. Our approach focuses on delivering a polynomial time and near-optimal solution, needed for expected large volume of data and its associated large-scale query processing.