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Discovering Expressive Process Models by Clustering Log Traces
August 2006 (vol. 18 no. 8)
pp. 1010-1027
ASCII Text
x 
 
Gianluigi Greco, Antonella Guzzo, Luigi Pontieri, Domenico Sacc?, "Discovering Expressive Process Models by Clustering Log Traces," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 8, pp. 1010-1027, August, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2006.123,
author = {Gianluigi Greco and Antonella Guzzo and Luigi Pontieri and Domenico Sacc?},
title = {Discovering Expressive Process Models by Clustering Log Traces},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {18},
number = {8},
issn = {1041-4347},
year = {2006},
pages = {1010-1027},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.123},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Discovering Expressive Process Models by Clustering Log Traces
IS - 8
SN - 1041-4347
SP1010
EP1027
EPD - 1010-1027
A1 - Gianluigi Greco,
A1 - Antonella Guzzo,
A1 - Luigi Pontieri,
A1 - Domenico Sacc?,
PY - 2006
KW - Process mining
KW - data mining
KW - workflow management
KW - clustering
KW - classification
KW - association rules.
VL - 18
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Process mining techniques have recently received notable attention in the literature for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches, where the identification of different variants for the process is explicitly accounted for, based on the clustering of log traces. Indeed, modeling each group of similar executions with a different schema allows us to single out "conformant” models, which, specifically, minimize the number of modeled enactments that are extraneous to the process semantics. Therefore, a novel process mining framework is introduced and some relevant computational issues are deeply studied. As finding an exact solution to such an enhanced process mining problem is proven to require high computational costs, in most practical cases, a greedy approach is devised. This is founded on an iterative, hierarchical, refinement of the process model, where, at each step, traces sharing similar behavior patterns are clustered together and equipped with a specialized schema. The algorithm guarantees that each refinement leads to an increasingly sound model, thus attaining a monotonic search. Experimental results evidence the validity of the approach with respect to both effectiveness and scalability.

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Index Terms:
Process mining, data mining, workflow management, clustering, classification, association rules.
Citation:
Gianluigi Greco, Antonella Guzzo, Luigi Pontieri, Domenico Sacc?, "Discovering Expressive Process Models by Clustering Log Traces," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 8, pp. 1010-1027, Aug. 2006, doi:10.1109/TKDE.2006.123
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