Logic-Based Incremental Process Mining
Abstract
Manually building process models is complex, costly and error-prone. Hence, the interest in process mining. Incremental adaptation of the models, and the ability to express/learn complex conditions on the involved tasks, are also desirable. First-order logic provides a single comprehensive and powerful framework for supporting all of the above. This paper presents a First-Order Logic incremental method for inferring process models. Its efficiency and effectiveness were proved with both controlled experiments and a real-world dataset.
Cite
Text
Ferilli et al. "Logic-Based Incremental Process Mining." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23461-8_17Markdown
[Ferilli et al. "Logic-Based Incremental Process Mining." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/ferilli2015ecmlpkdd-logicbased/) doi:10.1007/978-3-319-23461-8_17BibTeX
@inproceedings{ferilli2015ecmlpkdd-logicbased,
title = {{Logic-Based Incremental Process Mining}},
author = {Ferilli, Stefano and Redavid, Domenico and Esposito, Floriana},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {218-221},
doi = {10.1007/978-3-319-23461-8_17},
url = {https://mlanthology.org/ecmlpkdd/2015/ferilli2015ecmlpkdd-logicbased/}
}