Authors:
Jean Michel Lau
1
;
Cirano Iochpe
1
;
Lucinéia Heloisa Thom
2
and
Manfred Reichert
2
Affiliations:
1
Federal University of Rio Grande do Sul, Brazil
;
2
University of Ulm, Germany
Keyword(s):
Business process modeling, Workflow activity patterns, Knowledge discovery, Data mining, Reuse.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Business Process Management
;
CASE Tools for System Development
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Requirements Analysis And Management
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Research on workflow activity patterns recently emerged in order to increase the reuse of recurring business functions (e.g., notification, approval, and decision). One important aspect is to identify pattern co-occurrences and to utilize respective information for creating modeling recommendations regarding the most suited activity patterns to be combined with an already used one. Activity patterns as well as their co-occurrences can be identified through the analysis of process models rather than event logs. Related to this problem, this paper proposes a method for discovering and analyzing activity pattern co-occurrences in business process models. Our results are used for developing a BPM tool which fosters the modeling of business processes based on the reuse of activity patterns. Our tool includes an inference engine which considers the patterns co-occurrences to give design time recommendations for pattern usage.