The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Hadoop GIS: a high performance spatial data warehousing system over mapreduce
Open accessAbstract
Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The R*-tree: an efficient and robust access method for points and rectangles
Beckmann, N., Kriegel, H.-P., Schneider, R., & Seeger, B. (1990) · Proceedings of the 1990 ACM SIGMOD international conference on Management of data · doi:10.1145/93597.98741 · Cited by 4,161
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
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
MapReduce: a flexible data processing tool
Dean, J., & Ghemawat, S. (2010) · Communications of the ACM · doi:10.1145/1629175.1629198 · Cited by 1,182
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
HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads
Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D., Silberschatz, A., & Rasin, A. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687731 · Cited by 857
SCOPE: easy and efficient parallel processing of massive data sets
Chaiken, R., Jenkins, B., Larson, P.-Å., Ramsey, B., Shakib, D., Weaver, S., & Zhou, J. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1454159.1454166 · Cited by 727
MapReduce and parallel DBMSs: friends or foes?
Stonebraker, M., Abadi, D., DeWitt, D. J., Madden, S., Paulson, E., Pavlo, A., & Rasin, A. (2010) · Communications of the ACM · doi:10.1145/1629175.1629197 · Cited by 430
A comparison of join algorithms for log processing in MaPreduce
Blanas, S., Patel, J. M., Ercegovac, V., Rao, J., Shekita, E. J., & Tian, Y. (2010) · Proceedings of the 2010 ACM SIGMOD International Conference on Management of data · doi:10.1145/1807167.1807273 · Cited by 414
Building a high-level dataflow system on top of Map-Reduce: the Pig experience
Gates, A. F., Natkovich, O., Chopra, S., Kamath, P., Narayanamurthy, S. M., Olston, C., Reed, B., Srinivasan, S., & Srivastava, U. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687553.1687568 · Cited by 405
Cited by
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
Pantanowitz, L., Sharma, A., Carter, A. B., Kurc, T., Sussman, A., & Saltz, J. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_69_18 · Cited by 282
Self-citationSpatiotemporal 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
Hamraz, H., Contreras, M. A., & Zhang, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-07200-0 · Cited by 97
Metadata
- Authors
- Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., & Saltz, J.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2013-08
- Volume
- 6
- Issue
- 11
- Pages
- 1009-1020
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W2115583184
- Cited by
- 593
- References
- 13
- FWCI
- 93.94
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hadoop GIS: a high performance spatial data warehousing system over mapreduce. https://app.cochat.ai/papers/10.14778/2536222.2536227
- The paper
- Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., & Saltz, J. (2013). Hadoop GIS: a high performance spatial data warehousing system over mapreduce. Proceedings of the VLDB Endowment. https://doi.org/10.14778/2536222.2536227
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
