Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

Hdl Handle:
http://hdl.handle.net/10545/620827
Title:
Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications
Authors:
Liu, Jia ( 0000-0002-2736-431X ) ; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yincui; Hill, Richard; Guang, Jie; Li, Chi
Abstract:
Workflow for remote sensing quantitative retrieval is the “bridge” between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation
Affiliation:
University of Derby
Citation:
Liu, J. et al (2017) 'Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications', Computers & Geosciences 98:46
Publisher:
Elsevier
Journal:
Computers & Geosciences
Issue Date:
Jan-2017
URI:
http://hdl.handle.net/10545/620827
DOI:
10.1016/j.cageo.2016.10.002
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0098300416305155
Type:
Article
Language:
en
ISSN:
00983004
Appears in Collections:
Department of Electronics, Computing & Maths

Full metadata record

DC FieldValue Language
dc.contributor.authorLiu, Jiaen
dc.contributor.authorLiu, Longlien
dc.contributor.authorXue, Yongen
dc.contributor.authorDong, Jingen
dc.contributor.authorHu, Yincuien
dc.contributor.authorHill, Richarden
dc.contributor.authorGuang, Jieen
dc.contributor.authorLi, Chien
dc.date.accessioned2016-11-14T12:17:46Z-
dc.date.available2016-11-14T12:17:46Z-
dc.date.issued2017-01-
dc.identifier.citationLiu, J. et al (2017) 'Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications', Computers & Geosciences 98:46en
dc.identifier.issn00983004-
dc.identifier.doi10.1016/j.cageo.2016.10.002-
dc.identifier.urihttp://hdl.handle.net/10545/620827-
dc.description.abstractWorkflow for remote sensing quantitative retrieval is the “bridge” between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validationen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0098300416305155en
dc.rightsArchived with thanks to Computers & Geosciencesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectWorkflowen
dc.subjectValidationen
dc.subjectTacit knowledgeen
dc.subjectOntologyen
dc.titleGrid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applicationsen
dc.typeArticleen
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalComputers & Geosciencesen
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