Paper Info
Title
T-GOWler: Discovering Generalized Process Models Within Texts.
Abstract
Contemporary workflow management systems are driven by explicit process models specifying the interdependencies between tasks. Creating these models is a challenging and timeconsuming task. Existing approaches to mining concrete workflows into models tackle design aspects related to the diverging abstraction levels of the tasks. Concrete workflow logs represent tasks and cases of concrete events-partially or totally ordered-grounding hidden multilevel (abstract) semantics and contexts. Relevant generalized events could be rediscovered within these processes. We propose, in this article, an ontology-based workflow mining systemto generate patterns fromsequences of events that are themselves extracted from texts. Our system T-GOWler (Generalized Ontology-basedWorkfLow minER within Texts) is based on two ontology-basedmodules: a workflow extractor and a patternminer. To this end, it uses two different ontologies: a domain one (to support workflow extraction from texts) and a processual one (to mine generalized patterns from extracted workflows).
Year
DOI
Venue
2017
10.1089/cmb.2017.0085
JOURNAL OF COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
ontology,phylogenetic analyses,text mining,workflow mining
Ontology (information science),Ontology,Workflow technology,Computer science,Process modeling,Bioinformatics,XPDL,Workflow management system,Workflow,Semantics
Journal
Volume
Issue
ISSN
24.0
8
1066-5277
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
Ahmed Halioui141.46
Petko Valtchev290272.38
Abdoulaye Baniré Diallo3409.37