DocumentCode :
187441
Title :
A novel approach to process mining: Intentional process models discovery
Author :
Khodabandelou, Ghazaleh ; Hug, Charlotte ; Salinesi, Camille
Author_Institution :
Centre de Rech. en Inf., Univ. Paris 1 Pantheon-Sorbonne, Paris, France
fYear :
2014
fDate :
28-30 May 2014
Firstpage :
1
Lastpage :
12
Abstract :
So far, process mining techniques have suggested to model processes in terms of tasks that occur during the enactment of a process. However, research on method engineering and guidance has illustrated that many issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. This paper presents a novel approach of process mining, called Map Miner Method (MMM). This method is designed to automate the construction of intentional process models from process logs. MMM uses Hidden Markov Models to model the relationship between users´ activities logs and the strategies to fulfill their intentions. The method also includes two specific algorithms developed to infer users´ intentions and construct intentional process model (Map) respectively. MMM can construct Map process models with different levels of abstraction (fine-grained and coarse-grained process models) with respect to the Map metamodel formalism (i.e., metamodel that specifies intentions and strategies of process actors). This paper presents all steps toward the construction of Map process models topology. The entire method is applied on a large-scale case study (Eclipse UDC) to mine the associated intentional process. The likelihood of the obtained process model shows a satisfying efficiency for the proposed method.
Keywords :
data mining; hidden Markov models; learning (artificial intelligence); MMM; Map metamodel formalism; hidden Markov models; intentional process model construction; intentional process model discovery; map miner method; map process models topology; process logs; process mining; Adaptation models; Computational modeling; Hidden Markov models; Information systems; Measurement; Topology; Intention-oriented Process Modeling; Process Mining; unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Research Challenges in Information Science (RCIS), 2014 IEEE Eighth International Conference on
Conference_Location :
Marrakech
Type :
conf
DOI :
10.1109/RCIS.2014.6861040
Filename :
6861040
Link To Document :
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