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Data warehousing concept has metamorphosed from the information warehouse of the 1970’s. Over the past decade both data warehouse and data mart, two main data warehousing school of thoughts, have evolved into a £ 66 billion industry. The data warehouse or data mart is in essence a specialised database which stores meaningful historical data. This state of the art technology is vital for analytical tasks and decision making activities of many corporations. The analytical data derive from the data warehousing technology is achievable with OLAP technology. However, there are ample of evidences in the literature which support the lack of research within some of the spheres of these technologies. One key area which merits a closer investigation is the topic of data warehouse designs.
The investigation study also extends to include OLAP technology. Throughout this investigation, the data mart is referred to as a data warehouse system (DWS). The data warehouse designs under investigation are the: star schema; snowflake schema and starflake schema. Three OLAP types are scrutinised following the implementation of the DWS. The OLAP types are: MOLAP, ROLAP and HOLAP. Both data warehouse designs and OLAP are investigated in terms of performance and storage efficiency. The measured performances are: processing time and average query response time (AQRT). The study concludes with some interesting findings based on results, analysis and a devised DW design & OLAP rating scale. The DWS’ dataset on which the rating scale is based contains 3.45 million records.
The star schema with MOLAP system has the best AQRT performance. Similarly, the starflake schema which uses HOLAP has both the best processing performance and storage efficiency. The snowflake schema with ROLAP has the worst overall performance. Finally, based on the above rating scale, the star schema with MOLAP is highly recommended to the data warehouse users.
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