Home // International Journal On Advances in Software, volume 7, numbers 1 and 2, 2014 // View article

Instance-Based Integration of Multidimensional Data Models

Authors:
Michael Mireku Kwakye
Iluju Kiringa
Herna L. Viktor

Keywords: Schema Merging, Data Integration, Model Management, Mapping Modelling Constraints, Multidimensional Merge Algorithm, Data Warehousing

Abstract:
Meta-model merging is the process of incorporating data models into an integrated, consistent model, against which accurate queries may be processed. The efficiency of such a process is very much reliant on effective semantic representation of chosen data models, as well as the mapping relationships between the schema and data instance elements of the data models. Within the data warehousing domain, the integration of data marts is often time-consuming. Intuitively forming an all-inclusive data warehouse presents tedious tasks of identifying related fact and dimension table attributes. Moreover, the ability to process queries across these disparate, but related, data marts poses an important challenge. In this paper, we introduce an approach for the integration of relational star schemas, which are instances of multidimensional data models. These instance schemas represented as data marts are integrated into a single consolidated data warehouse. Our methodology, which is based on model management operations, focuses on a formulated merge algorithm and adopts first-order Global-and-Local-As-View (GLAV) mapping models, to deliver a polynomial time, near-optimal solution of a single integrated enterprise-wide data warehouse.

Pages: 402 to 421

Copyright: Copyright (c) to authors, 2014. Used with permission.

Publication date: June 30, 2014

Published in: journal

ISSN: 1942-2628