Toward Developing Data Warehousing Process Standards: An Ontology-Based Review of Existing Methodologies

Abstract

A data warehouse is developed using a data warehousing process (DWP) methodology. Currently, there are a large number of methodologies available in the data warehousing market. The reason for this is the lack of any centralized attempts at creating platform-independent DWP standards. For the development of such standards, it is very important that we first examine the current practices being followed by the data warehousing industry. In this study, we review 30 commercial data warehousing methodologies and analyze the standard practices they have adopted with respect to DWP. To perform the analysis, we first develop an ontological model of DWP based on a thorough review of the literature and inputs from experts in the data warehousing field. The ontological model consists of two hierarchies: a composition hierarchy which shows the decomposition of DWP tasks such as system development, extract, transform, and load (ETL), and end-user application design; and a classification hierarchy which specifies the alternative methods or techniques available for performing the tasks. We next apply hierarchical cluster analysis to group the methodologies that share a common set of standards. Our study provides valuable insights into the prevailing standard practices for different DWP tasks-system development, requirements analysis, architecture design, data modeling, ETL, data extraction, and end-user application design-and identifies important directions for future research on DWP standardization

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