Abstract
Spatial data plays a pivotal role in decision-making applications in a way that nowadays we witness its ever-growing and unprecedented use in both analyses and decision-making. In between, spatial relations constitute a significant form of human understanding of spatial formation. Regarding this, the relationships between spatial objects, particularly topological relations, have recently received considerable attention. However, real-world spatial regions such as lakes or forests have no exact boundaries and are considered fuzzy. Therefore, defining fuzzy relationships between them would yield better results. So far, several types of research have addressed this issue, and remarkable advances have been achieved. In this paper, we propose a novel method to model the “Part” relation of fuzzy region connection calculus (RCC) relations. Furthermore, a method based on fuzzy RCC relations for fuzzification of an important group of spatial queries, namely the skyline operator, is proposed in spatial databases that can be used in decision support, data visualization, and spatial databases applications. The proposed algorithms have been implemented and evaluated on real-world spatial datasets. The results of the carried out evaluation demonstrate more flexibility in comparison with other well-established existing methods, as well as the appropriateness of the speed and quality of the results.
Original language | English |
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Title of host publication | Intelligent Computing: Proceedings of the 2019 Computing Conference. Volume 1 |
Editors | Kohei Arai, Rahul Bhatia, Supriya Kapoor |
Place of Publication | Switzerland |
Publisher | Springer |
Pages | 659-677 |
Number of pages | 19 |
ISBN (Electronic) | 9783030228712 |
ISBN (Print) | 9783030228705 |
DOIs | |
Publication status | Published - 2019 |