Abstract
With the massive increase in the usage of location-based services, there has been a huge increase in the availability of spatial data. Extracting hidden business value inherent in the spatial data like the migration of the customer base etc. has become mandate. With the recent advancement of open-source distributed computing techniques like Hadoop the computing power is made available at ease. SpatialHadoop, Hive, Impala are the popular tools used for querying spatial data. These tools generally use indexing methods to execute queries. Extensive work on optimizing joins has been done, but as the real-world spatial datasets contain huge skew, optimizing spatial joins is still a challenging problem. We investigate the problem of skew present in the spatial datasets by providing skew aware partitioning techniques for multi-way spatial joins. We solve the problem by distributing the data symmetrically across the cluster nodes. Our algorithms implemented in Hadoop mapreduce framework offers skew aware partitioning techniques by further reducing the communication cost. We implemented a binary split partitioning approach and strip partitioned technique for multi-way spatial join and compared with the baseline approaches sequential join and controlled replicate. We observed that our approaches are in line with the existing approaches for uniformly distributed datasets, whereas for the skewed datasets, our techniques outperformed the exiting techniques. Our experiments indicate that effective partitioning strategies, distribute the data evenly across the reducers, and have a better overall turn around time compared to the baseline methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aji, A., Hoang, V., Wang, F.: Effective spatial data partitioning for scalable query processing (2015)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090. ACM (2011)
Gupta, H., Chawda, B., Negi, S., Faruquie, T.A., Subramaniam, L.V., Mohania, M.: Processing multi-way spatial joins on map-reduce. In: Proceedings of the 16th International Conference on Extending Database Technology, pp. 113–124. ACM (2013)
Madurika, H., Hemakumara, G.: Gis based analysis for suitability location finding in the residential development areas of greater matara region. Int. J. Sci. Technol. Res 4, 96–105 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kadari, P., Potluri, A., Sristy, N.B., Subramanyam, R.B.V., Kumar, N.V.N. (2020). Skew Aware Partitioning Techniques for Multi-way Spatial Join. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-66187-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66186-1
Online ISBN: 978-3-030-66187-8
eBook Packages: Computer ScienceComputer Science (R0)