The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
SpatialHadoop: A MapReduce framework for spatial data
Abstract
This paper describes SpatialHadoop; a full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and expressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. Other spatial operations are also implemented following a similar approach. Extensive experiments on real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.
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Cites
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
R-trees: a dynamic index structure for spatial searching
Guttman, A. (1984) · Proceedings of the 1984 ACM SIGMOD international conference on Management of data - SIGMOD '84 · doi:10.1145/602259.602266 · Cited by 6,580
Pig latin: a not-so-foreign language for data processing
Olston, C., Reed, B., Srivastava, U., Kumar, R., & Tomkins, A. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376726 · Cited by 1,730
R-trees: a dynamic index structure for spatial searching
Guttman, A. (1984) · ACM SIGMOD Record · doi:10.1145/971697.602266 · Cited by 1,612
Hive: a warehousing solution over a map-reduce framework
Thusoo, A., Sarma, J. S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., & Murthy, R. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687553.1687609 · Cited by 1,556
A comparison of approaches to large-scale data analysis
Pavlo, A., Paulson, E., Rasin, A., Abadi, D. J., DeWitt, D. J., Madden, S., & Stonebraker, M. (2009) · Proceedings of the 2009 ACM SIGMOD International Conference on Management of data · doi:10.1145/1559845.1559865 · Cited by 1,071
Hadoop GIS: a high performance spatial data warehousing system over mapreduce
Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., & Saltz, J. (2013) · Proceedings of the VLDB Endowment · doi:10.14778/2536222.2536227 · Cited by 593
SystemML: Declarative machine learning on MapReduce
Ghoting, A., Krishnamurthy, R., Pednault, E., Reinwald, B., Sindhwani, V., Tatikonda, S., Tian, Y., & Vaithyanathan, S. (2011) · 2011 IEEE 27th International Conference on Data Engineering · doi:10.1109/icde.2011.5767930 · Cited by 295
Efficient processing of k nearest neighbor joins using MapReduce
Lu, W., Shen, Y., Chen, S., & Ooi, B. C. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2336664.2336674 · Cited by 250
Efficient parallel kNN joins for large data in MapReduce
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A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation
Yuan, H., & Li, G. (2021) · Data Science and Engineering · doi:10.1007/s41019-020-00151-z · Cited by 389
Spatiotemporal Data Mining: A Computational Perspective
Shekhar, S., Jiang, Z., Ali, R., Eftelioglu, E., Tang, X., Gunturi, V., & Zhou, X. (2015) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi4042306 · Cited by 206
Metadata
- Authors
- Eldawy, A., & Mokbel, M. F.
- Venue
- 2015 IEEE 31st International Conference on Data Engineering
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 1352-1363
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W1568832590
- Cited by
- 547
- References
- 13
- FWCI
- 56.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Eldawy, A., & Mokbel, M. F. (2015). SpatialHadoop: A MapReduce framework for spatial data. 2015 IEEE 31st International Conference on Data Engineering. https://doi.org/10.1109/icde.2015.7113382
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
