Chaudhuri, S. ; Narasayya, V. ; Sarawagi, S. (2002) Efficient evaluation of queries with mining predicates In: 18th International Conference on Data Engineering.
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Official URL: http://doi.org/10.1109/ICDE.2002.994772
Related URL: http://dx.doi.org/10.1109/ICDE.2002.994772
Abstract
Modern relational database systems are beginning to support ad-hoc queries on data mining models. In this paper, we explore novel techniques for optimizing queries that apply mining models to relational data. For such queries, we use the internal structure of the mining model to automatically derive traditional database predicates. We present algorithms for deriving such predicates for some popular discrete mining models: decision trees, naive Bayes, and clustering. Our experiments on a Microsoft SQL Server 2000 demonstrate that these derived predicates can significantly reduce the cost of evaluating such queries.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to IEEE |
| Keywords: | Predictive models;Chromium;Relational databases;Data mining;Business;Clustering algorithms;Costs;Postal services;Filtering;Engines |
| ID Code: | 128418 |
| Deposited On: | 20 Oct 2022 09:09 |
| Last Modified: | 14 Nov 2022 11:45 |
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