Abstract
Nowadays, with the continuously increasing volume of spatial data, it is difficult to execute spatial queries efficiently in spatial data-intensive applications, because of the limited computational capability and storage resources of centralized environments. Due to that, shared-nothing spatial cloud infrastructures have received increasing attention in the last years. SpatialHadoop is a full-edged MapReduce framework with native support for spatial data. SpatialHadoop also supports spatial indexing on top of Hadoop to perform efficiently spatial queries (e.g., k-Nearest Neighbor search, spatial intersection join, etc.). The Reverse k-Nearest Neighbor (RkNN) problem, i.e., finding all objects in a dataset that have a given query point among their corresponding k-nearest neighbors, has been recently studied very thoroughly. RkNN queries are of particular interest in a wide range of applications, such as decision support systems, resource allocation, profile-based marketing, location-based services, etc. In this paper, we present the design and implementation of an RkNN query MapReduce algorithm, so-called MRSLICE, in SpatialHadoop. We have evaluated the performance of the MRSLICE algorithm on SpatialHadoop with big real-world datasets. The experiments have demonstrated the efficiency and scalability of our proposal in comparison with other RkNNQ MapReduce algorithms in SpatialHadoop.
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- 1.
Available at http://spatialhadoop.cs.umn.edu/datasets.html.
- 2.
Available at https://github.com/aseldawy/spatialhadoop2.
References
Akdogan, A., Demiryurek, U., Kashani, F.B., Shahabi, C.: Voronoi-based geospatial query processing with MapReduce. In: CloudCom Conference, pp. 9–16 (2010)
Cheema, M.A., Lin, X., Zhang, W., Zhang, Y.: Influence zone: efficiently processing reverse k nearest neighbors queries. In: ICDE Conference, pp. 577–588 (2011)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: OSDI Conference, pp. 137–150 (2004)
Eldawy, A., Mokbel, M.F.: Spatialhadoop: a mapreduce framework for spatial data. In: ICDE Conference, pp. 1352–1363 (2015)
García-García, F., Corral, A., Iribarne, L., Vassilakopoulos, M.: RkNN query processing in distributed spatial infrastructures: a performance study. In: Ouhammou, Y., Ivanovic, M., Abelló, A., Bellatreche, L. (eds.) MEDI 2017. LNCS, vol. 10563, pp. 200–207. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66854-3_15
Ji, C., Hu, H., Xu, Y., Li, Y., Qu, W.: Efficient multi-dimensional spatial RkNN query processing with MapReduce. In: ChinaGrid Conference, pp. 63–68 (2013)
Ji, C., Qu, W., Li, Z., Xu, Y., Li, Y., Wu, J.: Scalable multi-dimensional RNN query processing. Concurrency Comput.: Pract. Experience 27(16), 4156–4171 (2015)
Korn, F., Muthukrishnan, S.: Influence sets based on reverse nearest neighbor queries. In: SIGMOD Conference, pp. 201–212 (2000)
Singh, A., Ferhatosmanoglu, H., Tosun, A.S.: High dimensional reverse nearest neighbor queries. In: CIKM Conference, pp. 91–98 (2003)
Song, W., Qin, J., Wang, W., Cheema, M.A.: Pre-computed region guardian sets based reverse kNN queries. In: DASFAA Conference, pp. 98–112 (2016)
Stanoi, I., Agrawal, D., El Abbadi, A.: Reverse nearest neighbor queries for dynamic databases. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 44–53 (2000)
Tao, Y., Papadias, D., Lian, X.: Reverse kNN search in arbitrary dimensionality. In: VLDB Conference, pp. 744–755 (2004)
Wu, W., Yang, F., Chan, C.Y., Tan, K.: FINCH: evaluating reverse k-nearest-neighbor queries on location data. PVLDB 1(1), 1056–1067 (2008)
Yang, S., Cheema, M.A., Lin, X., Wang, W.: Reverse K nearest neighbors query processing: Experiments and analysis. PVLDB 8(5), 605–616 (2015)
Yang, S., Cheema, M.A., Lin, X., Zhang, Y.: SLICE: reviving regions-based pruning for reverse k nearest neighbors queries. In: ICDE Conference, pp. 760–771 (2014)
Acknowledgments
Research of all authors is supported by the MINECO research project [TIN2017-83964-R].
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García-García, F., Corral, A., Iribarne, L., Vassilakopoulos, M. (2019). MRSLICE: Efficient RkNN Query Processing in SpatialHadoop. In: Schewe, KD., Singh, N. (eds) Model and Data Engineering. MEDI 2019. Lecture Notes in Computer Science(), vol 11815. Springer, Cham. https://doi.org/10.1007/978-3-030-32065-2_17
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DOI: https://doi.org/10.1007/978-3-030-32065-2_17
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