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  - Rozinat, A
A1  - Van der Aalst, Wil M. P.
T1  - Conformance testing: Measuring the fit and appropriateness of event logs and process models
N2  - Most information systems log events (e.g., transaction logs, audit traits) to audit and monitor the processes they support. At the same time, many of these processes have been explicitly modeled. For example, SAP R/3 logs events in transaction logs and there are EPCs (Event-driven Process Chains) describing the so-called reference models. These reference models describe how the system should be used. The coexistence of event logs and process models raises an interesting question: "Does the event log conform to the process model and vice versa?". This paper demonstrates that there is not a simple answer to this question. To tackle the problem, we distinguish two dimensions of conformance: fitness (the event log may be the result of the process modeled) and appropriateness (the model is a likely candidate from a structural and behavioral point of view). Different metrics have been defined and a Conformance Checker has been implemented within the ProM Framework
Y1  - 2006
ER  - 
TY  - JOUR
A1  - Weske, Mathias
A1  - van der Aalst, Wil M. P.
A1  - Verbeek, H. M. W.
T1  - Advances in business process management
Y1  - 2004
SN  - 0169-023X
ER  - 
TY  - BOOK
A1  - Rogge-Solti, Andreas
A1  - Mans, Ronny S.
A1  - van der Aalst, Wil M. P.
A1  - Weske, Mathias
T1  - Repairing event logs using stochastic process models
N2  - Companies strive to improve their business processes in order to remain competitive. Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient’s treatment. As a result, events or timestamps may be missing or incorrect. In this paper, we make use of process knowledge captured in process models, and provide a method to repair missing events in the logs. This way, we facilitate analysis of incomplete logs. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks. We evaluate the results using both synthetic data and real event data from a Dutch hospital.
N2  - Unternehmen optimieren ihre Geschäftsprozesse laufend um im kompetitiven Umfeld zu bestehen. Das Ziel von Process Mining ist es, bedeutende Erkenntnisse aus prozessrelevanten Daten zu extrahieren. In den letzten Jahren sorgte Process Mining bei Experten, Werkzeugherstellern und Forschern zunehmend für Aufsehen. Traditionell wird dabei angenommen, dass Ereignisprotokolle die tatsächliche Ist-Situation widerspiegeln. Dies ist jedoch nicht unbedingt der Fall, wenn prozessrelevante Ereignisse manuell erfasst werden. Ein Beispiel hierfür findet sich im Krankenhaus, in dem das Personal Behandlungen meist manuell dokumentiert. Vergessene oder fehlerhafte Einträge in Ereignisprotokollen sind in solchen Fällen nicht auszuschließen. In diesem technischen Bericht wird eine Methode vorgestellt, die das Wissen aus Prozessmodellen und historischen Daten nutzt um fehlende Einträge in Ereignisprotokollen zu reparieren. Somit wird die Analyse unvollständiger Ereignisprotokolle erleichtert. Die Reparatur erfolgt mit einer Kombination aus stochastischen Petri Netzen, Alignments und Bayes'schen Netzen. Die Ergebnisse werden mit synthetischen Daten und echten Daten eines holländischen Krankenhauses evaluiert.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 78 
KW  - Process Mining
KW  - fehlende Daten
KW  - stochastische Petri Netze
KW  - Bayes'sche Netze
KW  - process mining
KW  - missing data
KW  - stochastic Petri nets
KW  - Bayesian networks
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-66797
SN  - 978-3-86956-258-2
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  -