Authors:
Francesco Folino
1
;
Gianluigi Greco
2
;
Antonella Guzzo
3
and
Luigi Pontieri
1
Affiliations:
1
ICAR-CNR, Italy
;
2
UNICAL, Italy
;
3
DEIS, UNICAL, Italy
Keyword(s):
Business Process Intelligence, Process Mining, Decision Trees.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Business Process Management
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Industrial Applications of Artificial Intelligence
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Process Mining techniques exploit the information stored in the executions log of a process in order to extract some high-level process model, which can be used for both analysis and design tasks. Most of these techniques focus on “structural” (control-flow oriented) aspects of the process, in that they only consider what elementary activities were executed and in which ordering. In this way, any other “non-structural” information, usually kept in real log systems (e.g., activity executors, parameter values, and time-stamps), is completely disregarded, yet being a potential source of knowledge. In this paper, we overcome this limitation by proposing a novel approach for discovering process models, where the behavior of a process is characterized from both structural and non-structural viewpoints. In a nutshell, different variants of the process (classes) are recognized through a structural clustering approach, and represented with a collection of specific workflow models. Relevant co
rrelations between these classes and non-structural properties are made explicit through a rule-based classification model, which can be exploited for both explanation and prediction purposes. Results on real-life application scenario evidence that the discovered models are often very accurate and capture important knowledge on the process behavior.
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