Abstract
The explosive growth in data collection in business organizations introduces the problem of turning these rapidly expanding data stores into nuggets of actionable knowledge. The state-of-the-art data mining tools available for this integrate loosely with data stored in DMBSSs, typically through a cursor interface. In this paper, we consider several formulations of association rule mining (a typical data mining problem) using SQL-92 queries and study the performance of different join orders and join methods for executing them. We analyze the cost of the different execution plans which provides a basis to incorporate the semantics of association rule mining into future query optimizers. Based on them we identify certain optimizations and develop the Set-oriented Apriori approach. This work is an initial step towards developing “SQL-aware” mining algorithms and exploring the enhancements to current relational DBMSs to make them “mining-aware” thereby bridging the gap between the two.
This work was supported in part by the Office of Naval Research and the Spawar System Center — San Diego, by the Rome Laboratory, DARPA, and the NSF Grant IRI-9528390
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Thomas, S., Chakravarthy, S. (1999). Performance Evaluation and Optimization of Join Queries for Association Rule Mining. In: Mohania, M., Tjoa, A.M. (eds) DataWarehousing and Knowledge Discovery. DaWaK 1999. Lecture Notes in Computer Science, vol 1676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48298-9_26
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DOI: https://doi.org/10.1007/3-540-48298-9_26
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