WoMan: Logic-Based Workflow Learning and Management

Workflow management is fundamental to efficiently, effectively, and economically carry out complex working and domestic activities. Manual engineering of workflow models is a complex, costly, and error-prone task. The WoMan framework for workflow management is based on first-order logic. Its core is...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on systems, man, and cybernetics. Systems Jg. 44; H. 6; S. 744 - 756
1. Verfasser: Ferilli, Stefano
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.06.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
[D.1.6] Logic programming
[I.2.6.g] machine learning
[M.7.0.a] business process modeling
Adaptation models
Algorithms
Applied sciences
Artificial intelligence
Automation
Biological system modeling
Computer science; control theory; systems
Economics
Exact sciences and technology
Information systems. Data bases
International trade
Learning
Logic
Memory organisation. Data processing
Meteorology
Noise
Petri nets
Software
Workflow
[M.7.0.a] business process modeling
[D.1.6] Logic programming
[I.2.6.g] machine learning
Groupware
Energy consumption
Algorithmics
Workflow
Logical programming
Case based reasoning
Integrated management
Modeling
Business process
Business model
First order logic
Firm management
Artificial intelligence
Monitoring
Quantitative analysis
Petri net
ISSN:2168-2216, 2168-2232
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Workflow management is fundamental to efficiently, effectively, and economically carry out complex working and domestic activities. Manual engineering of workflow models is a complex, costly, and error-prone task. The WoMan framework for workflow management is based on first-order logic. Its core is an automatic procedure that learns and refines workflow models from observed cases of process execution. Its innovative peculiarities include incrementality (allowing quick learning even in the presence of noise and changed behavior), strict adherence to the observed practices, ability to learn complex conditions for the workflow components, and improved expressive power compared to the state of the art. This paper presents the entire algorithmic apparatus of WoMan, including translation and learning from a standard log format for case representation, import/export of workflow models from/into standard formalisms (Petri nets), and exploitation of the learned models for process simulation and monitoring. Qualitative and quantitative experimental evaluation shows the power and efficiency of WoMan, both in controlled and in real-world domains.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2013.2273310