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

Jia Liu, Longli Liu, Yong Xue, Jing Dong, Yingcui Hu, Richard Hill, Jie Guang, Chi Li

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)46-54
Number of pages9
JournalComputers and Geosciences
Volume98
Early online date11 Oct 2016
DOIs
Publication statusPublished - Jan 2017
Externally publishedYes

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