T-GOWler: Discovering Generalized Process Models Within Texts
| Autor: | Abdoulaye Baniré Diallo, Ahmed Halioui, Petko Valtchev |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Process modeling Computer science media_common.quotation_subject Ontology (information science) Workflow XPDL Workflow technology 03 medical and health sciences Genetics Data Mining Humans Molecular Biology Phylogeny media_common Workflow mining Computational Biology Data science Semantics Interdependence Computational Mathematics Gene Ontology 030104 developmental biology Computational Theory and Mathematics Modeling and Simulation Algorithms Workflow management system |
| Zdroj: | Journal of Computational Biology. 24:799-808 |
| ISSN: | 1557-8666 |
| DOI: | 10.1089/cmb.2017.0085 |
| Popis: | Contemporary workflow management systems are driven by explicit process models specifying the interdependencies between tasks. Creating these models is a challenging and time-consuming 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 system to generate patterns from sequences of events that are themselves extracted from texts. Our system T-GOWler (Generalized Ontology-based WorkfLow minER within Texts) is based on two ontology-based modules: a workflow extractor and a pattern miner. 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). |
| Databáze: | OpenAIRE |
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