A MapReduce-Based Big Spatial Data Framework for Solving the Problem of Covering a Polygon with Orthogonal Rectangles

Abstract: The polygon covering problem is an important class of problems in the area of computational geometry. There are slightly different versions of this problem
depending on the types of polygons to be addressed. In this paper, we focus on finding an answer to a question of whether an orthogonal rectangle, or spatial query window,
is fully covered by a set of orthogonal rectangles which are in smaller sizes. This problem is encountered in many application domains including object
recognition/extraction/trace, spatial analyses, topological analyses, and augmented reality applications. In many real-world applications, in the cases of using traditional
central computation techniques, working with real world data results in a performance bottlenecks. The work presented in this paper proposes a high performance
MapReduce-based big data framework to solve the polygon covering problem in the cases of using a spatial query window and data are represented as a set of orthogonal
rectangles. Orthogonal rectangular polygons are represented in the form of minimum bounding boxes. The spatial query windows are also called as range queries. The
proposed spatial big data framework is evaluated in terms of horizontal scalability. In addition, efficiency and speed-up performance metrics for the proposed two algorithms
are measured.


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