Abstract
The polygon retrieval problem is, in essence, the problem of preprocessing a set of n 2-dimensional points, so than given a special ContainedIn spatial query, the subset of points falling inside the polygon can be reported efficiently. Such queries find great applicability in areas such as computer graphics, spatial databases and GIS applications. However, as the size of spatial data grows rapidly existing centralized solutions fail to retrieve the results in reasonable response time. In this paper, we propose a novel MapReduce algorithm for efficiently processing convex polygon planar range queries in a distributed manner. We apply a grid-based and an angle-based partitioning scheme on the data space and perform a comparative analysis. Through our experimental evaluation we prove that our system is efficient, robust and scalable.
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Acknowledgements
This research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: Thales. Investing in knowledge society through the European Social Fund.
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Nodarakis, N., Sioutas, S., Gerolymatos, P., Tsakalidis, A., Tzimas, G. (2016). Convex Polygon Planar Range Queries on the Cloud: Grid vs. Angle-Based Partitioning. In: Karydis, I., Sioutas, S., Triantafillou, P., Tsoumakos, D. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2015. Lecture Notes in Computer Science(), vol 9511. Springer, Cham. https://doi.org/10.1007/978-3-319-29919-8_9
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DOI: https://doi.org/10.1007/978-3-319-29919-8_9
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