TY - JOUR A1 - Mendling, Jan A1 - Weber, Ingo A1 - van der Aalst, Wil A1 - Brocke, Jan Vom A1 - Cabanillas, Cristina A1 - Daniel, Florian A1 - Debois, Soren A1 - Di Ciccio, Claudio A1 - Dumas, Marlon A1 - Dustdar, Schahram A1 - Gal, Avigdor A1 - Garcia-Banuelos, Luciano A1 - Governatori, Guido A1 - Hull, Richard A1 - La Rosa, Marcello A1 - Leopold, Henrik A1 - Leymann, Frank A1 - Recker, Jan A1 - Reichert, Manfred A1 - Reijers, Hajo A. A1 - Rinderle-Ma, Stefanie A1 - Solti, Andreas A1 - Rosemann, Michael A1 - Schulte, Stefan A1 - Singh, Munindar P. A1 - Slaats, Tijs A1 - Staples, Mark A1 - Weber, Barbara A1 - Weidlich, Matthias A1 - Weske, Mathias A1 - Xu, Xiwei A1 - Zhu, Liming T1 - Blockchains for Business Process Management BT - Challenges and Opportunities JF - ACM Transactions on Management Information Systems N2 - Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM. KW - Blockchain KW - business process management KW - research challenges Y1 - 2018 U6 - https://doi.org/10.1145/3183367 SN - 2158-656X SN - 2158-6578 VL - 9 IS - 1 SP - 1 EP - 16 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - van der Aa, Han A1 - Leopold, Henrik A1 - Weidlich, Matthias T1 - Partial order resolution of event logs for process conformance checking JF - Decision support systems : DSS N2 - While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably. KW - process mining KW - conformance checking KW - partial order resolution KW - data KW - uncertainty Y1 - 2020 U6 - https://doi.org/10.1016/j.dss.2020.113347 SN - 0167-9236 SN - 1873-5797 VL - 136 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Leopold, Henrik A1 - van der Aa, Han A1 - Offenberg, Jelmer A1 - Reijers, Hajo A. T1 - Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels JF - Information systems N2 - Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects. KW - Label analysis KW - Process model KW - Natural language KW - Hidden Markov models Y1 - 2019 U6 - https://doi.org/10.1016/j.is.2019.02.005 SN - 0306-4379 SN - 1873-6076 VL - 83 SP - 30 EP - 39 PB - Elsevier 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 - TY - GEN A1 - Han van der, Aa A1 - Di Ciccio, Claudio A1 - Leopold, Henrik A1 - Reijers, Hajo A. T1 - Extracting Declarative Process Models from Natural Language T2 - Advanced Information Systems Engineering (CAISE 2019) N2 - Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. Since the manual elicitation and creation of process models is a time-intensive endeavor, a variety of techniques have been developed that automatically derive process models from textual process descriptions. However, these techniques, so far, only focus on the extraction of traditional, imperative process models. The extraction of declarative process models, which allow to effectively capture complex process behavior in a compact fashion, has not been addressed. In this paper we close this gap by presenting the first automated approach for the extraction of declarative process models from natural language. To achieve this, we developed tailored Natural Language Processing techniques that identify activities and their inter-relations from textual constraint descriptions. A quantitative evaluation shows that our approach is able to generate constraints that closely resemble those established by humans. Therefore, our approach provides automated support for an otherwise tedious and complex manual endeavor. KW - Declarative modelling KW - Natural language processing KW - Model extraction Y1 - 2019 SN - 978-3-030-21290-2 SN - 978-3-030-21289-6 U6 - https://doi.org/10.1007/978-3-030-21290-2_23 SN - 0302-9743 SN - 1611-3349 VL - 11483 SP - 365 EP - 382 PB - Springer CY - Cham ER - TY - JOUR A1 - Leopold, Henrik A1 - Mendling, Jan A1 - Guenther, Oliver T1 - Learning from Quality Issues of BPMN Models from Industry JF - IEEE software N2 - Many organizations use business process models to document business operations and formalize business requirements in software-engineering projects. The Business Process Model and Notation (BPMN), a specification by the Object Management Group, has evolved into the leading standard for process modeling. One challenge is BPMN's complexity: it offers a huge variety of elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN use from the practitioners' perspective are still missing. To close this gap, researchers analyzed 585 BPMN 2.0 process models from six companies. They found that split and join representations, message flow, the lack of proper model decomposition, and labeling related to quality issues. They give five specific recommendations on how to avoid these issues. KW - process model quality KW - modeling guidelines KW - Business Process Model and Notation KW - BPMN KW - industry study KW - software engineering KW - software development Y1 - 2016 U6 - https://doi.org/10.1109/MS.2015.81 SN - 0740-7459 SN - 1937-4194 VL - 33 SP - 26 EP - 33 PB - Inst. of Electr. and Electronics Engineers CY - Los Alamitos ER - TY - JOUR A1 - Günther, Oliver A1 - Leopold, Henrik A1 - Mendling, Jan T1 - Learning from quality issues of BPMN models from industry BT - extended abstract JF - CEUR Workshop Proceedings N2 - Many organizations use business process models for documenting their business operations. In recent years, the Business Process Model and Notation (BPMN) evolved into the leading standard for process modeling. However, BPMN is complex: The specification offers a huge variety of different elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN usage from the perspective of practitioners are still missing. We close this gap by analyzing a large set of BPMN 2.0 process models from practice. We found that particularly representational choices for splits and joins, the correct use of message flow, the proper decomposition of models, and the consistent labeling appear to be connected with quality issues. Based on our findings we give five recommendations how these issues can be avoided in the future. The work summarized in this extended abstract has been published in [LMG16]. KW - BPMN modeling guidelines KW - Modeling recommendations KW - Process model quality Y1 - 2016 UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-84996503810&origin=inward&txGid=98101b239363e3f806d7fadf22f788e2 SN - 1613-0073 VL - 1701 SP - 36 EP - 38 ER - TY - JOUR A1 - Günther, Oliver A1 - Leopold, Henrik A1 - Mendling, Jan T1 - Learning from quality issues of BPMN models from industry JF - IEEE Software N2 - Many organizations use business process models to document business operations and formalize business requirements in software-engineering projects. The Business Process Model and Notation (BPMN), a specification by the Object Management Group, has evolved into the leading standard for process modeling. One challenge is BPMN's complexity: it offers a huge variety of elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN use from the practitioners' perspective are still missing. To close this gap, researchers analyzed 585 BPMN 2.0 process models from six companies. They found that split and join representations, message flow, the lack of proper model decomposition, and labeling related to quality issues. They give five specific recommendations on how to avoid these issues. Y1 - 2016 U6 - https://doi.org/10.1109/MS.2015.81 SN - 0740-7459 VL - 33 IS - 4 SP - 26 EP - 33 PB - Inst. of Electr. and Electronics Engineers CY - Los Alamitos ER - TY - JOUR A1 - Koorn, Jelmer Jan A1 - Lu, Xixi A1 - Leopold, Henrik A1 - Reijers, Hajo A. T1 - From action to response to effect BT - mining statistical relations in work processes JF - Information systems : IS ; an international journal ; data bases N2 - Process mining techniques are valuable to gain insights into and help improve (work) processes. Many of these techniques focus on the sequential order in which activities are performed. Few of these techniques consider the statistical relations within processes. In particular, existing techniques do not allow insights into how responses to an event (action) result in desired or undesired outcomes (effects). We propose and formalize the ARE miner, a novel technique that allows us to analyze and understand these action-response-effect patterns. We take a statistical approach to uncover potential dependency relations in these patterns. The goal of this research is to generate processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. We evaluate the ARE miner in two ways. First, we use an artificial data set to demonstrate the effectiveness of the ARE miner compared to two traditional process-oriented approaches. Second, we apply the ARE miner to a real-world data set from a Dutch healthcare institution. We show that the ARE miner generates comprehensible representations that lead to informative insights into statistical relations between actions, responses, and effects. KW - Process discovery KW - Statistical process mining KW - Effect measurement Y1 - 2022 U6 - https://doi.org/10.1016/j.is.2022.102035 SN - 0306-4379 SN - 0094-453X VL - 109 PB - Elsevier CY - Amsterdam ER -