@inproceedings{736c1bc9b35243adab9eacb63bb321af,
title = "Towards semantic process mining through knowledge-based trace abstraction",
abstract = "Many information systems nowadays record data about the process instances executed at the organization in the form of traces in a log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in the medical domain of stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.",
keywords = "Knowledge-based trace abstraction, Medical applications, Semantic process mining",
author = "G. Leonardi and M. Striani and S. Quaglini and A. Cavallini and S. Montani",
note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2019.; 7th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017 ; Conference date: 06-12-2017 Through 08-12-2017",
year = "2019",
doi = "10.1007/978-3-030-11638-5_3",
language = "English",
isbn = "9783030116378",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer Verlag",
pages = "45--64",
editor = "{van Keulen}, Maurice and Paolo Ceravolo and Kilian Stoffel",
booktitle = "Data-Driven Process Discovery and Analysis - 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers",
address = "Germany",
}