An experimental study of big spatial data systems

Hulbert, Andrew; Kunicki, T.; Hughes, James; Fox, Anthony D.; Eichelberger, Christopher N.

doi:10.1109/bigdata.2016.7840909

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…In [14] are presented and evaluated two distributed database technologies, GeoMESA which focuses on geotemporal indexes and Elasticsearch which is a document oriented data store that can handle arbitrary data which may have a geospatial index. In general GeoMesa is an open-source, distributed, spatio-temporal database built on a number of distributed cloud data storage systems, including Accumulo, HBase, Cassandra, and Kafka.…”

Section: Distributed Systems and Technologies For Spatial Data Processingmentioning

confidence: 99%

MongoDB Vs PostgreSQL: A comparative study on performance aspects

et al. 2020

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Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. MongoDB and PostgreSQL. The evaluation is based upon real, business scenarios and their subsequent queries as well as their underlying infrastructures and concludes in confirming the superiority of PostgreSQL in almost all cases with the exception of the polygon intersection queries. Furthermore, the average response time is radically reduced with the use of indexes, especially in the case of MongoDB.

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Section: Distributed Systems and Technologies For Spatial Data Processingmentioning

confidence: 99%

MongoDB Vs PostgreSQL: A comparative study on performance aspects

et al. 2020

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“…HBase intrinsically applies lossless compression by about a factor of 3, which compensates for the triple redundancy to ensure availability and fault tolerance. Another open-source technology commonly used for vector data is GeoMesa, which is able to use HBase as its backend to scalably store and process vector data or bounding box polygons for images [5,6]. GeoMesa is an integral part of the system.…”

Section: Ibm Pairs Geoscopementioning

confidence: 99%

PAIRS (RE)LOADED: SYSTEM DESIGN & BENCHMARKING FOR SCALABLE GEOSPATIAL APPLICATIONS

Albrecht

Bobroff

Elmegreen

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper we benchmark a previously introduced big data platform that enables the analysis of big data from remote sensing and other geospatial-temporal data. The platform, called IBM PAIRS Geoscope, has been developed by leveraging open source big data technologies (Hadoop/HBase) that are in principle scalable in storage and compute to hundreds of PetaBytes. Currently, PAIRS hosts multiple PetaBytes of curated and geospatial-temporally indexed data. It organizes all data with key-value combinations, performing analytics close to the data to minimize data movement.

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“…It offers Geohash Information 2018, 9, 116 4 of 21 (Z-order) spatial-prefix-based indexes that work for points, lines and polygons. Hulbert et al [22] demonstrated that ElasticSearch performed worse than GeoMesa in the number of result records and latency when performing larger spatio-temporal queries. This is because GeoMesa is underlying the distributed data store Accumulo, which enables a high-performance scalable space-filling curve index and allows for returning data in parallel.…”

Section: Related Workmentioning

confidence: 99%

Vector Spatial Big Data Storage and Optimized Query Based on the Multi-Level Hilbert Grid Index in HBase

Hua

Kang

et al. 2018

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Abstract:Faced with the rapid growth of vector data and the urgent requirement of low-latency query, it has become an important and timely challenge to effectively achieve the scalable storage and efficient access of vector big data. However, a systematic method is rarely seen for vector polygon data storage and query taking spatial locality into account in the storage schema, index construction and query optimization. In the paper, we focus on the storage and topological query of vector polygon geometry data in HBase, and the rowkey in the HBase table is the concatenation of the Hilbert value of the grid cell to which the center of the object entity's MBR belongs, the layer identifier and the order code. Then, a new multi-level grid index structure, termed Q-HBML, that incorporates the grid-object spatial relationship and a new Hilbert hierarchical code into the multi-level grid, is proposed for improving the spatial query efficiency. Finally, based on the Q-HBML index, two query optimization strategies and an optimized topological query algorithm, ML-OTQ, are presented to optimize the topological query process and enhance the topological query efficiency. Through four groups of comparative experiments, it has been proven that our approach supports better performance.

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An experimental study of big spatial data systems

Cited by 16 publications

References 8 publications

MongoDB Vs PostgreSQL: A comparative study on performance aspects

MongoDB Vs PostgreSQL: A comparative study on performance aspects

PAIRS (RE)LOADED: SYSTEM DESIGN & BENCHMARKING FOR SCALABLE GEOSPATIAL APPLICATIONS

Vector Spatial Big Data Storage and Optimized Query Based on the Multi-Level Hilbert Grid Index in HBase

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