Intelligent Database Designs and Query Considerations

Database optimization is one of the major concerns and the most important factor that needs to be considered when designing a very large database system. Database optimization and evaluation are usually concentrated on the query language and the organization of data. The recent advances in technologies of data-warehousing architecture, online analytical processing (OLAP), and data mining demand more rigorous optimization and evaluation strategies that involve the entire data-warehousing environment. While there are standard approaches for optimizing database queries and data structures separately, there appears to be no current and coherent research efforts in assessing optimization techniques in data-warehousing systems. Instead of concentrating on query language and data structure in optimizing a database, our research focused on all the qualitative elements or optimization factors in the entire data-warehousing system. That includes the language, the data, the architecture, as well as the operation. Our previous research [14] developed an approach to evaluate an information system qualitatively using Kiviat graphs. In this paper, we will use the Kiviat graphs to profile a datawarehousing system so that the results can be used for performance evaluation, comparison, and tuning.