TY  - JOUR
A1  - Bano, Dorina
A1  - Michael, Judith
A1  - Rumpe, Bernhard
A1  - Varga, Simon
A1  - Weske, Mathias
T1  - Process-aware digital twin cockpit synthesis from event logs
JF  - Journal of computer languages
N2  - The engineering of digital twins and their user interaction parts with explicated processes, namely processaware digital twin cockpits (PADTCs), is challenging due to the complexity of the systems and the need for information from different disciplines within the engineering process. Therefore, it is interesting to investigate how to facilitate their engineering by using already existing data, namely event logs, and reducing the number of manual steps for their engineering. Current research lacks systematic, automated approaches to derive process-aware digital twin cockpits even though some helpful techniques already exist in the areas of process mining and software engineering. Within this paper, we present a low-code development approach that reduces the amount of hand-written code needed and uses process mining techniques to derive PADTCs. We describe what models could be derived from event log data, which generative steps are needed for the engineering of PADTCs, and how process mining could be incorporated into the resulting application. This process is evaluated using the MIMIC III dataset for the creation of a PADTC prototype for an automated hospital transportation system. This approach can be used for early prototyping of PADTCs as it needs no hand-written code in the first place, but it still allows for the iterative evolvement of the application. This empowers domain experts to create their PADTC prototypes.
KW  - process-aware digital twin cockpit
KW  - low-code development approaches
KW  - sensor data
KW  - event log
KW  - process mining
KW  - process-awareness
Y1  - 2022
U6  - https://doi.org/10.1016/j.cola.2022.101121
SN  - 2590-1184
SN  - 2665-9182
VL  - 70
PB  - Elsevier
CY  - Amsterdam [u.a.]
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  -