Standard
Scalable dynamic business process discovery with the constructs competition miner. / Redlich, David; Molka, Thomas; Gilani, Wasif et al.
Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), Milan, Italy, November 19-21, 2014.. ed. / Rafael Accorsi; Paolo Ceravolo; Barbara Russo. 2014. p. 91-107.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Redlich, D, Molka, T, Gilani, W
, Blair, GS & Rashid, A 2014,
Scalable dynamic business process discovery with the constructs competition miner. in R Accorsi, P Ceravolo & B Russo (eds),
Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), Milan, Italy, November 19-21, 2014.. pp. 91-107. <
http://ceur-ws.org/Vol-1293/paper7.pdf>
APA
Redlich, D., Molka, T., Gilani, W.
, Blair, G. S., & Rashid, A. (2014).
Scalable dynamic business process discovery with the constructs competition miner. In R. Accorsi, P. Ceravolo, & B. Russo (Eds.),
Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), Milan, Italy, November 19-21, 2014. (pp. 91-107)
http://ceur-ws.org/Vol-1293/paper7.pdfVancouver
Redlich D, Molka T, Gilani W
, Blair GS, Rashid A.
Scalable dynamic business process discovery with the constructs competition miner. In Accorsi R, Ceravolo P, Russo B, editors, Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), Milan, Italy, November 19-21, 2014.. 2014. p. 91-107
Author
Bibtex
@inproceedings{d37cc1e4af4c4e31861459765a745dfe,
title = "Scalable dynamic business process discovery with the constructs competition miner",
abstract = "Since the environment for businesses is becoming more competitiveby the day, business organizations have to be more adaptive toenvironmental changes and are constantly in a process of optimization.Fundamental parts of these organizations are their business processes.Discovering and understanding the actual execution flow of the processesdeployed in organizations is an important enabler for the management,analysis, and optimization of both, the processes and the business. Thishas become increasingly difficult since business processes are now oftendynamically changing and may produce hundreds of events per second.The basis for this paper is the Constructs Competition Miner (CCM): Adivide-and-conquer algorithm which discovers block-structured processesfrom event logs possibly consisting of exceptional behaviour. In this paperwe propose a set of modifications for the CCM to enable scalabledynamic business process discovery of a run-time process model froma stream of events. We describe the different modifications and carryout an evaluation, investigating the behaviour of the algorithm on eventstreams of dynamically changing processes.",
keywords = "run-time models, business process management, process mining, complex event processing, event streaming , big data",
author = "David Redlich and Thomas Molka and Wasif Gilani and Blair, {Gordon S.} and Awais Rashid",
year = "2014",
language = "English",
pages = "91--107",
editor = "Rafael Accorsi and Paolo Ceravolo and Barbara Russo",
booktitle = "Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), Milan, Italy, November 19-21, 2014.",
}
RIS
TY - GEN
T1 - Scalable dynamic business process discovery with the constructs competition miner
AU - Redlich, David
AU - Molka, Thomas
AU - Gilani, Wasif
AU - Blair, Gordon S.
AU - Rashid, Awais
PY - 2014
Y1 - 2014
N2 - Since the environment for businesses is becoming more competitiveby the day, business organizations have to be more adaptive toenvironmental changes and are constantly in a process of optimization.Fundamental parts of these organizations are their business processes.Discovering and understanding the actual execution flow of the processesdeployed in organizations is an important enabler for the management,analysis, and optimization of both, the processes and the business. Thishas become increasingly difficult since business processes are now oftendynamically changing and may produce hundreds of events per second.The basis for this paper is the Constructs Competition Miner (CCM): Adivide-and-conquer algorithm which discovers block-structured processesfrom event logs possibly consisting of exceptional behaviour. In this paperwe propose a set of modifications for the CCM to enable scalabledynamic business process discovery of a run-time process model froma stream of events. We describe the different modifications and carryout an evaluation, investigating the behaviour of the algorithm on eventstreams of dynamically changing processes.
AB - Since the environment for businesses is becoming more competitiveby the day, business organizations have to be more adaptive toenvironmental changes and are constantly in a process of optimization.Fundamental parts of these organizations are their business processes.Discovering and understanding the actual execution flow of the processesdeployed in organizations is an important enabler for the management,analysis, and optimization of both, the processes and the business. Thishas become increasingly difficult since business processes are now oftendynamically changing and may produce hundreds of events per second.The basis for this paper is the Constructs Competition Miner (CCM): Adivide-and-conquer algorithm which discovers block-structured processesfrom event logs possibly consisting of exceptional behaviour. In this paperwe propose a set of modifications for the CCM to enable scalabledynamic business process discovery of a run-time process model froma stream of events. We describe the different modifications and carryout an evaluation, investigating the behaviour of the algorithm on eventstreams of dynamically changing processes.
KW - run-time models
KW - business process management
KW - process mining
KW - complex event processing
KW - event streaming
KW - big data
M3 - Conference contribution/Paper
SP - 91
EP - 107
BT - Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), Milan, Italy, November 19-21, 2014.
A2 - Accorsi, Rafael
A2 - Ceravolo, Paolo
A2 - Russo, Barbara
ER -
