Computing Trace Alignment against Declarative Process Models through Planning. Giacomo, G. D., Maggi, F. M., Marrella, A., & Sardina, S. In Paper abstract bibtex Process mining techniques aim at extracting non-trivial knowledge from event traces, which record the concrete execution of business processes. Typically, traces are "dirty" and contain spurious events or miss relevant events. Trace alignment is the problem of cleaning such traces against a process specification. There has recently been a growing use of declarative process models, e.g., Declare (based on LTL over finite traces) to capture constraints on the allowed task flows. We demonstrate here how state-of-the-art classical planning technologies can be used for trace alignment by presenting a suitable encoding. We report experimental results using a real log from a financial domain.
@inproceedings {icaps16-91,
track = {Applications Track},
title = {Computing Trace Alignment against Declarative Process Models through Planning},
url = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13094},
author = {Giuseppe De Giacomo and Fabrizio Maria Maggi and Andrea Marrella and Sebastian Sardina},
abstract = {Process mining techniques aim at extracting non-trivial knowledge from event traces, which record the concrete execution of business processes. Typically, traces are "dirty" and contain spurious events or miss relevant events. Trace alignment is the problem of cleaning such traces against a process specification. There has recently been a growing use of declarative process models, e.g.,
Declare (based on LTL over finite traces) to capture constraints on the allowed task flows. We demonstrate here how state-of-the-art classical planning technologies can be used for trace alignment by presenting a suitable encoding. We report experimental results using a real log from a financial domain.},
keywords = {Evaluation; testing; and validation of P&S applications,Description and modeling of novel application domains}
}
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