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 - BOOK A1 - Smirnov, Sergey A1 - Reijers, Hajo A. A1 - Nugteren, Thijs A1 - Weske, Mathias T1 - Business process model abstraction : theory and practice N2 - Business process management aims at capturing, understanding, and improving work in organizations. The central artifacts are process models, which serve different purposes. Detailed process models are used to analyze concrete working procedures, while high-level models show, for instance, handovers between departments. To provide different views on process models, business process model abstraction has emerged. While several approaches have been proposed, a number of abstraction use case that are both relevant for industry and scientifically challenging are yet to be addressed. In this paper we systematically develop, classify, and consolidate different use cases for business process model abstraction. The reported work is based on a study with BPM users in the health insurance sector and validated with a BPM consultancy company and a large BPM vendor. The identified fifteen abstraction use cases reflect the industry demand. The related work on business process model abstraction is evaluated against the use cases, which leads to a research agenda. T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 35 Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-41782 SN - 978-3-86956-054-0 PB - Universitätsverlag Potsdam CY - Potsdam 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 - 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 - 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 -