On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment
Abstract
One major task in business process management is that of aligning real process execution traces to a process model by (minimally) introducing and eliminating steps. Here, we look at declarative process specifications expressed in Linear Temporal Logic on finite traces (LTLf). We provide a sound and complete technique to synthesize the alignment instructions relying on finite automata theoretic manipulations. Such a technique can be effectively implemented by using planning technology. Notably, the resulting planning-based alignment system significantly outperforms all current state-of-the-art ad-hoc alignment systems. We report an in-depth experimental study that supports this claim.
Cite
Text
De Giacomo et al. "On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11020Markdown
[De Giacomo et al. "On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/giacomo2017aaai-disruptive/) doi:10.1609/AAAI.V31I1.11020BibTeX
@inproceedings{giacomo2017aaai-disruptive,
title = {{On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment}},
author = {De Giacomo, Giuseppe and Maggi, Fabrizio Maria and Marrella, Andrea and Patrizi, Fabio},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {3555-3561},
doi = {10.1609/AAAI.V31I1.11020},
url = {https://mlanthology.org/aaai/2017/giacomo2017aaai-disruptive/}
}