TY - JOUR A1 - Baier, Thomas A1 - Di Ciccio, Claudio A1 - Mendling, Jan A1 - Weske, Mathias T1 - Matching events and activities by integrating behavioral aspects and label analysis JF - Software and systems modeling N2 - Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs. KW - Process mining KW - Event mapping KW - Business process intelligence KW - Constraint satisfaction KW - Declare KW - Natural language processing Y1 - 2018 U6 - https://doi.org/10.1007/s10270-017-0603-z SN - 1619-1366 SN - 1619-1374 VL - 17 IS - 2 SP - 573 EP - 598 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Baier, Thomas A1 - Mendling, Jan A1 - Weske, Mathias T1 - Bridging abstraction layers in process mining JF - Information systems N2 - While the maturity of process mining algorithms increases and more process mining tools enter the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Current approaches for event log abstraction try to abstract from the events in an automated way that does not capture the required domain knowledge to fit business activities. This can lead to misinterpretation of discovered process models. We developed an approach that aims to abstract an event log to the same abstraction level that is needed by the business. We use domain knowledge extracted from existing process documentation to semi-automatically match events and activities. Our abstraction approach is able to deal with n:m relations between events and activities and also supports concurrency. We evaluated our approach in two case studies with a German IT outsourcing company. (C) 2014 Elsevier Ltd. All rights reserved. KW - Process mining KW - Abstraction KW - Event mapping Y1 - 2014 U6 - https://doi.org/10.1016/j.is.2014.04.004 SN - 0306-4379 SN - 1873-6076 VL - 46 SP - 123 EP - 139 PB - Elsevier CY - Oxford ER -