Incrementally Maintaining Classification using an RDBMS

Mehmet Levent Koc; Christopher Ré

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arXiv · 1103.3107

Incrementally Maintaining Classification using an RDBMS

Mehmet Levent Koc·Christopher Ré

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Abstract

The proliferation of imprecise data has motivated both researchers and the database industry to push statistical techniques into relational database management systems (RDBMSs). We study algorithms to maintain model-based views for a popular statistical technique, classification, inside an RDBMS in the presence of updates to the training examples. We make three technical contributions: (1) An algorithm that incrementally maintains classification inside an RDBMS. (2) An analysis of the above algorithm that shows that our algorithm is optimal among all deterministic algorithms (and asymptotically within a factor of 2 of a nondeterministic optimal). (3) An index structure based on the technical ideas that underlie the above algorithm which allows us to store only a fraction of the entities in memory. We apply our techniques to text processing, and we demonstrate that our algorithms provide several orders of magnitude improvement over non-incremental approaches to classification on a variety of data sets: such as the Cora, UCI Machine Learning Repository data sets, Citeseer, and DBLife.

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Mehmet Levent Koc, Christopher Ré. 2011-04-16. Incrementally Maintaining Classification using an RDBMS. https://arxiv.org/abs/1103.3107

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