Direction based method for representing and querying fuzzy regions

Uncertainty management for geometric data is a fundamental issue for spatial databases, image databases, and spatial data systems, such as geographic information systems. Currently, spatial database systems can only handle geographical applications that interact with discrete spatial objects. In reality, many spatial objects do not have uniform interiors and sharp limits but rather have interiors and bounds that are partial, uncertain, or fuzzy. However, authors have numerous debates over the definition of the fuzzy region, fuzzy points, and lines. A method for modeling 2D fuzzy regions is introduced based on the proposed direction concept. The method, membership function, and fuzzy spatial set operations are formally defined and implemented using SQL Server 2014. The proposed method is compared with available methods of the grid (bitmap), vector-map, and extended triangulated irregular networks (ETIN) in terms of memory complexity, time complexity, and the accuracy of data storage (making noise), and its applicability. This proposed method outperforms the memory complexity of grid, vector-map, and ETIN methods, the time complexity of vector-map and ETIN methods, and the accuracy of the grid and vector-map methods. The image processing issues are not the subjects of this study.