Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications
Journal article
Liu, Jia, Liu, Longli, Xue, Yong, Dong, Jing, Hu, Yincui, Hill, Richard, Guang, Jie and Li, Chi 2016. Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2016.10.002
| Authors | Liu, Jia, Liu, Longli, Xue, Yong, Dong, Jing, Hu, Yincui, Hill, Richard, Guang, Jie and 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 |
| Keywords | Workflow; Validation; Tacit knowledge; Ontology |
| Year | 2016 |
| Journal | Computers & Geosciences |
| Publisher | Elsevier |
| ISSN | 00983004 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cageo.2016.10.002 |
| Web address (URL) | http://hdl.handle.net/10545/620827 |
| http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| hdl:10545/620827 | |
| Publication dates | 11 Oct 2016 |
| Publication process dates | |
| Deposited | 14 Nov 2016, 12:17 |
| Accepted | 09 Oct 2016 |
| Rights | Archived with thanks to Computers & Geosciences |
| Contributors | University of Derby |
| File | File Access Level Open |
| File | File Access Level Open |
| File | File Access Level Open |
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