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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 222-225 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.118

A Schema Selection Framework for Data Warehouse Design

M. H. Peyravi
Abstract—Data schema represents the arrangement of fact table and dimension tables and the relations between them. In data warehouse development, selecting a right and appropriate data schema (Snowflake, Star, Star Cluster …) has an important Impact on performance and usability of the designed data warehouse. One of the problems that exists in data warehouse development is lack of a comprehensive and sound selection framework to choose an appropriate schema for the data warehouse at hand by considering application domain-specific conditions. In this paper, we present a schema selection framework that is based on a decision tree for solving the problem of choosing right schema for a data warehouse. The main selection criteria that are used in the presented decision tree are query type, attribute type, dimension table type and existence of index. To evaluate correctness and soundness of this framework, we have designed a test bed that includes multiple data warehouses and we have created all the possible states in decision tree of schema selection framework. Then we designed all types of queries and performed the designed queries on these data warehouses. The results confirm the correct functionality of the schema selection framework.

Index Terms—Data warehouse, framework, online transaction processing, schema selection.

M. H. Peyravi is with Department Of Computer & Science, Sarvestan Branch, Islamic Azad University, Fars, Iran (email:Peyravi@iausarv.ac.ir).

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Cite:M. H. Peyravi, "A Schema Selection Framework for Data Warehouse Design," International Journal of Machine Learning and Computing vol.2, no. 3, pp. 222-225, 2012.

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