@inproceedings{aa804cef4eac43828e0e51c5f1ee97bb,
title = "Translating process mining results into intelligible business information",
abstract = "Most business processes are today rooted into an informa-tion system recording operational events in log files. Process Mining algorithms exploit this information to discover and qualify differences between observed and modelled process. However, the output of these algorithms are not clearly con-nected with business properties. Our work faces these lim-itations by proposing an approach for calibrating Process Mining results based on the Business Rules adopted by an organisation. The general idea relates on applying Process Mining algorithms on subsequent refinements of the event log, flltering process executions based on Business Rules. This way we are able to associate these results with specific characterisations of the process, as entailed by the corre-sponding Business Rules. This approach is confronted to a real world scenario using data provided by an Italian man-ufacturing company.",
keywords = "Business Process Assess-ment, Business Rules, Process Mining",
author = "Paolo Ceravolo and Antonia Azzini and Ernesto Damiani and Mariangela Lazoi and Manuela Marra and Angelo Corallo",
note = "Funding Information: This work was partly funded by the Italian Ministry of Economic Development under the Industria 2015 contract - KITE.IT project. Publisher Copyright: {\textcopyright} 2016 ACM.; 11th International Knowledge Management in Organizations Conference on the Changing Face of Knowledge Management Impacting Society, KMO 2016 ; Conference date: 25-07-2016 Through 28-07-2016",
year = "2016",
month = jul,
day = "25",
doi = "10.1145/2925995.2925997",
language = "British English",
series = "ACM International Conference Proceeding Series",
booktitle = "Proceedings of the 11th International Knowledge Management in Organizations Conference on the Changing Face of Knowledge Management Impacting Society, KMO 2016",
}