TY - JOUR A1 - Yousfi, Alaaeddine A1 - Weske, Mathias T1 - Discovering commute patterns via process mining JF - Knowledge and Information Systems N2 - Ubiquitous computing has proven its relevance and efficiency in improving the user experience across a myriad of situations. It is now the ineluctable solution to keep pace with the ever-changing environments in which current systems operate. Despite the achievements of ubiquitous computing, this discipline is still overlooked in business process management. This is surprising, since many of today’s challenges, in this domain, can be addressed by methods and techniques from ubiquitous computing, for instance user context and dynamic aspects of resource locations. This paper takes a first step to integrate methods and techniques from ubiquitous computing in business process management. To do so, we propose discovering commute patterns via process mining. Through our proposition, we can deduce the users’ significant locations, routes, travel times and travel modes. This information can be a stepping-stone toward helping the business process management community embrace the latest achievements in ubiquitous computing, mainly in location-based service. To corroborate our claims, a user study was conducted. The significant places, routes, travel modes and commuting times of our test subjects were inferred with high accuracies. All in all, ubiquitous computing can enrich the processes with new capabilities that go beyond what has been established in business process management so far. KW - Commute pattern KW - Commute process KW - Process mining KW - Ubiquitous computing KW - Location-based services Y1 - 2019 U6 - https://doi.org/10.1007/s10115-018-1255-1 SN - 0219-1377 SN - 0219-3116 VL - 60 IS - 2 SP - 691 EP - 713 PB - Springer CY - London 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 - 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 - Awad, Ahmed Mahmoud Hany Aly A1 - Gore, Rajeev A1 - Hou, Zhe A1 - Thomson, James A1 - Weidlich, Matthias T1 - An iterative approach to synthesize business process templates from compliance rules JF - INFORMATION SYSTEMS N2 - Companies have to adhere to compliance requirements. The compliance analysis of business operations is typically a joint effort of business experts and compliance experts. Those experts need to create a common understanding of business processes to effectively conduct compliance management. In this paper, we present a technique that aims at supporting this process. We argue that process templates generated out of compliance requirements provide a basis for negotiation among business and compliance experts. We introduce a semi-automated and iterative approach to the synthesis of such process templates from compliance requirements expressed in Linear Temporal Logic (LTL). We show how generic constraints related to business process execution are incorporated and present criteria that point at underspecification. Further, we outline how such underspecification may be resolved to iteratively build up a complete specification. For the synthesis, we leverage existing work on process mining and process restructuring. However, our approach is not limited to the control-flow perspective, but also considers direct and indirect data-flow dependencies. Finally, we elaborate on the application of the derived process templates and present an implementation of our approach. (C) 2012 Elsevier Ltd. All rights reserved. KW - Process synthesis KW - Analysis of business process compliance specification KW - Process mining Y1 - 2012 U6 - https://doi.org/10.1016/j.is.2012.05.001 SN - 0306-4379 VL - 37 IS - 8 SP - 714 EP - 736 PB - PERGAMON-ELSEVIER SCIENCE LTD CY - OXFORD ER - TY - JOUR A1 - Aa, Han van der A1 - Rebmann, Adrian A1 - Leopold, Henrik T1 - Natural language-based detection of semantic execution anomalies in event logs JF - Information systems : IS ; an international journal ; data bases N2 - Anomaly detection in process mining aims to recognize outlying or unexpected behavior in event logs for purposes such as the removal of noise and identification of conformance violations. Existing techniques for this task are primarily frequency-based, arguing that behavior is anomalous because it is uncommon. However, such techniques ignore the semantics of recorded events and, therefore, do not take the meaning of potential anomalies into consideration. In this work, we overcome this caveat and focus on the detection of anomalies from a semantic perspective, arguing that anomalies can be recognized when process behavior does not make sense. To achieve this, we propose an approach that exploits the natural language associated with events. Our key idea is to detect anomalous process behavior by identifying semantically inconsistent execution patterns. To detect such patterns, we first automatically extract business objects and actions from the textual labels of events. We then compare these against a process-independent knowledge base. By populating this knowledge base with patterns from various kinds of resources, our approach can be used in a range of contexts and domains. We demonstrate the capability of our approach to successfully detect semantic execution anomalies through an evaluation based on a set of real-world and synthetic event logs and show the complementary nature of semantics-based anomaly detection to existing frequency-based techniques. KW - Process mining KW - Natural language processing KW - Anomaly detection Y1 - 2021 U6 - https://doi.org/10.1016/j.is.2021.101824 SN - 0306-4379 SN - 1873-6076 VL - 102 PB - Elsevier CY - Amsterdam ER -