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Journal ArticleDOI

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

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TLDR
Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop and integrated into Hive to support declarative spatial queries with an integrated architecture is presented.
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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Proceedings ArticleDOI

A GPU-friendly Geometric Data Model and Algebra for Spatial Queries

TL;DR: A new model that represents spatial data as geometric objects and define an algebra consisting of GPU-friendly composable operators that operate over these objects is proposed, which shows that it is orders of magnitude faster than a CPU-based implementation and outperforms custom GPU-based approaches.
Journal ArticleDOI

Optimizing and accelerating space–time Ripley ’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis

TL;DR: A distributed computing method to accelerate space–time Ripley’s K function upon state-of-the-art distributed computing framework Apache Spark is presented, and four strategies are adopted to simplify calculation procedures and accelerate distributed computing respectively.
Proceedings ArticleDOI

On Spatial Joins in MapReduce

TL;DR: This paper provides the first attempt for a full-fledged query optimizer for MapReduce-based spatial join algorithms by developing its own taxonomy that covers almost all possible ways of doing a spatial join for any two input datasets.
Proceedings ArticleDOI

High performance integrated spatial big data analytics

TL;DR: A scalable spatial query based data integration engine with MapReduce is provided, and integrated spatial data analytics by consolidating multiple data sources provides significant potential for data quality improvement in terms of completeness and accuracy, and much increased values derived from the data.
Journal ArticleDOI

High Performance Processing and Analysis of Geospatial Data Using CUDA on GPU

TL;DR: In this article, high-performance processing of massive geospatial data on many-core GPU (Graphic Processing Unit) is presented using CUDA (Compute Unified Device Architecture) pro...
References
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