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  - 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  -