Abducing Compliance of Incomplete Event Logs
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arXiv
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| Format: | Preprint |
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2016
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| author | Chesani, Federico De Masellis, Riccardo Di Francescomarino, Chiara Ghidini, Chiara Mello, Paola Montali, Marco Tessaris, Sergio |
| author_facet | Chesani, Federico De Masellis, Riccardo Di Francescomarino, Chiara Ghidini, Chiara Mello, Paola Montali, Marco Tessaris, Sergio |
| contents | The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model. Nonetheless, these tools are often very rigid in dealing with with Event Logs that include incomplete information about the process execution. Thus, while the ability of handling incomplete event data is one of the challenges mentioned in the process mining manifesto, the evaluation of compliance of an execution trace still requires an end-to-end complete trace to be performed.
This paper exploits the power of abduction to provide a flexible, yet computationally effective, framework to deal with different forms of incompleteness in an Event Log. Moreover it proposes a refinement of the classical notion of compliance into strong and conditional compliance to take into account incomplete logs. Finally, performances evaluation in an experimental setting shows the feasibility of the presented approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1606_05446 |
| institution | arXiv |
| publishDate | 2016 |
| record_format | arxiv |
| spellingShingle | Abducing Compliance of Incomplete Event Logs Chesani, Federico De Masellis, Riccardo Di Francescomarino, Chiara Ghidini, Chiara Mello, Paola Montali, Marco Tessaris, Sergio Artificial Intelligence The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model. Nonetheless, these tools are often very rigid in dealing with with Event Logs that include incomplete information about the process execution. Thus, while the ability of handling incomplete event data is one of the challenges mentioned in the process mining manifesto, the evaluation of compliance of an execution trace still requires an end-to-end complete trace to be performed. This paper exploits the power of abduction to provide a flexible, yet computationally effective, framework to deal with different forms of incompleteness in an Event Log. Moreover it proposes a refinement of the classical notion of compliance into strong and conditional compliance to take into account incomplete logs. Finally, performances evaluation in an experimental setting shows the feasibility of the presented approach. |
| title | Abducing Compliance of Incomplete Event Logs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/1606.05446 |