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Article

Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method

by 1,2, 1,2,3,*, 1,2,3,4,5, 1,2, 6, 1,2 and 1,2,3
1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
School of Geography, Nanjing Normal University, Nanjing 210023, China
5
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
6
Institute of Earthquake Forecasting, The China Earthquake Administration, Beijing 100036, China
*
Author to whom correspondence should be addressed.
Academic Editors: Rob Brennan, Brian Davis, Armin Haller, Beyza Yaman and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(9), 605; https://doi.org/10.3390/ijgi10090605
Received: 2 July 2021 / Revised: 4 September 2021 / Accepted: 6 September 2021 / Published: 14 September 2021
(This article belongs to the Special Issue Semantic Spatial Web)
Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel method to formalize the parameter constraints of geoprocessing tools, based on a high-level and standard constraint language (i.e., SHACL) and geoprocessing ontologies, under the guidance of a systematic classification of parameter constraints. An application case and a heuristic evaluation were conducted to demonstrate and evaluate the effectiveness and usability of the proposed method. The results show that the proposed method is not only comparatively easier and more efficient than existing methods but also covers more types of parameter constraints, for example, the application-context-matching constraints that have been ignored by existing methods. View Full-Text
Keywords: intelligent geoprocessing; parameter constraints; input data validation; formalization; heuristic evaluation; application context intelligent geoprocessing; parameter constraints; input data validation; formalization; heuristic evaluation; application context
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MDPI and ACS Style

Hou, Z.-W.; Qin, C.-Z.; Zhu, A.-X.; Wang, Y.-J.; Liang, P.; Wang, Y.-J.; Zhu, Y.-Q. Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method. ISPRS Int. J. Geo-Inf. 2021, 10, 605. https://doi.org/10.3390/ijgi10090605

AMA Style

Hou Z-W, Qin C-Z, Zhu A-X, Wang Y-J, Liang P, Wang Y-J, Zhu Y-Q. Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method. ISPRS International Journal of Geo-Information. 2021; 10(9):605. https://doi.org/10.3390/ijgi10090605

Chicago/Turabian Style

Hou, Zhi-Wei, Cheng-Zhi Qin, A-Xing Zhu, Yi-Jie Wang, Peng Liang, Yu-Jing Wang, and Yun-Qiang Zhu. 2021. "Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method" ISPRS International Journal of Geo-Information 10, no. 9: 605. https://doi.org/10.3390/ijgi10090605

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