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Methods and techniques for discovering taxonomies of behavioral process models
Author(s) -
Folino Francesco,
Greco Gianluigi,
Guzzo Antonella,
Pontieri Luigi
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1081
Subject(s) - computer science , business process discovery , process mining , abstraction , business process management , workflow , process (computing) , surprise , data science , taxonomy (biology) , business process modeling , process modeling , business process , data mining , artificial intelligence , software engineering , work in process , database , programming language , engineering , psychology , social psychology , philosophy , operations management , botany , epistemology , biology
Abstract Modeling behavioral aspects of business processes is a hard and costly task, which usually requires heavy intervention of business experts. This explains the increasing attention given to process mining techniques, which automatically extract behavioral process models from log data. In the case of complex processes, however, the models identified by classical process mining techniques are hardly useful to analyze business operations at a suitable abstraction level. In fact, the need of process abstraction emerged in several application scenarios, and abstraction methods are already supported in some business‐management platforms, which allow users to manually define abstract views for the process at hand. Therefore, it comes with no surprise that process mining research recently considered the issue of mining processes at different abstraction levels, mainly in the form of a taxonomy of process models, as to overcome the drawbacks of traditional approaches. This paper presents a general framework for the discovery of such a taxonomy, and offers a survey of different kinds of basic techniques that can be exploited to this purpose: (1) workflow modeling and discovery techniques, (2) clustering techniques enabling the discovery of different behavioral process classes, and (3) activity abstraction techniques for associating a generalized process model with each higher level taxonomy node. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Classification