| Download | - View accepted manuscript: Bayesian classifcation of events for task labeling using workfow models (PDF, 701 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1007/978-3-642-00328-8_10 |
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| Author | Search for: Buffett, Scott1; Search for: Geng, Liqiang1 |
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| Affiliation | - National Research Council Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Conference | The 4th Workshop on Business Process Intelligence (BPI 08) in conjunction with Business Process Management (BPM 2008), 1 September 2008, Milan, Italy |
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| Subject | workflow; process mining; task labeling; Bayesian classification |
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| Abstract | We investigate a method designed to improve accuracy of workflow mining in the case that the identification of task labels for log events are uncertain. Here we consider how the accuracy of an independent task identifier, such as a classification or clustering engine, can be improved by examining workflow. After briefly introducing the notion of iterative workflow mining, where the mined workflow is used to help improve the true task labelings which, when re-mined, will produce a more accurate workflow model, we demonstrate a Bayesian updating approach to determining posterior probabilities for each label for a given event, by considering the probabilities from the previous step as well as information as to the beliefs of the labels that can be gained by examining the workflow model. Experiments show that labeling accuracy can be increased significantly, resulting in more accurate workflow models. |
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| Publication date | 2008-09 |
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| Publisher | Springer |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRCC 50389 |
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| NPARC number | 5764119 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 9f83c326-6d42-4eb3-956d-6218d1385ddf |
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| Record created | 2009-03-29 |
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| Record modified | 2020-04-15 |
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