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Applying Frequent Sequence Mining to Identify Design Flaws in Enterprise Software Systems (2007)
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| Venue: | In Proceedings 5th International Conference on Machine Learning and Data Mining (poster track |
| Citations: | 2 - 2 self |
BibTeX
@INPROCEEDINGS{Parsons07applyingfrequent,
author = {Trevor Parsons and John Murphy},
title = {Applying Frequent Sequence Mining to Identify Design Flaws in Enterprise Software Systems},
booktitle = {In Proceedings 5th International Conference on Machine Learning and Data Mining (poster track},
year = {2007}
}
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Abstract
Abstract. In this paper we show how frequent sequence mining (FSM) can be applied to data produced by monitoring distributed enterprise applications. In particular we show how we applied FSM to run-time paths to highlight repeating sequences of interest by using alternative support counting techniques. We show how the patterns identified, can be used to highlight design flaws in enterprise applications. We also discuss some algorithm scalability problems related to applying FSM to run-time paths and give solutions to these issues. 1
