@inproceedings{58f34bbe3c6a4051a466d97e7f783f9a,
title = "Identifying cohorts that differ in their behaviour: Tool support",
abstract = "Process mining is a specialised form of data analytics that aims to provide data-driven improvement recommendations, derived from event logs. These event logs contain information about the execution of real world processes, which may be complex. Cohort identification recommends drill down filters for process mining, based on differences in process. In this paper, we describe its integration in three process mining tools: as a stand-alone ProM plug-in, as part of the visual Miner and (planned) as part of Course Insights.",
keywords = "Feature selection, Filter recommendation, Process mining, Stochastic comparative Process mining",
author = "Leemans, \{Sander J.J.\} and Shiva Shabaninejad and Kanika Goel and Hassan Khosravi and Shazia Sadiq and Wynn, \{Moe T.\}",
note = "Publisher Copyright: Copyright {\textcopyright} 2020 for this paper by its authors.; 39th International Conference on Conceptual Modeling, ER 2020, ER 2020 ; Conference date: 03-11-2020 Through 06-11-2020",
year = "2020",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS",
pages = "163--167",
editor = "Judith Michael and Victoria Torres",
booktitle = "ER Forum, Demo and Posters 2020",
}