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  - 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  - 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  -