article

Hadoop GIS: a high performance spatial data warehousing system over mapreduce

Authors:

Xiaodong Zhang,

Joel SaltzAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 6, Issue 11

Pages 1009 - 1020

https://doi.org/10.14778/2536222.2536227

Published: 01 August 2013 Publication History

Abstract

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.

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Index Terms

Hadoop GIS: a high performance spatial data warehousing system over mapreduce
1. Human-centered computing
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 6, Issue 11

August 2013

237 pages

ISSN:2150-8097

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VLDB Endowment

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Published: 01 August 2013

Published in PVLDB Volume 6, Issue 11

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https://doi.org/10.5772/intechopen.1004870
Yang YZuo XZhao KLi Y(2024)Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory DataISPRS International Journal of Geo-Information10.3390/ijgi1306019713:6(197)Online publication date: 12-Jun-2024
https://doi.org/10.3390/ijgi13060197
Michalopoulos ATsitsigkos DBouros PMamoulis NTerrovitis M(2024)Efficient Distance Queries on Non-point DataACM Transactions on Spatial Algorithms and Systems10.1145/369819411:1(1-37)Online publication date: 2-Oct-2024
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Farhadloo MSharma AShekhar SMarkovic S(2024)Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR VisionACM Transactions on Spatial Algorithms and Systems10.1145/367920110:3(1-36)Online publication date: 25-Sep-2024
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