Enabling Standard Geospatial Capabilities in Spark for the Efficient Processing of Geospatial Big Data

Engélinus, Jonathan; Badard, Thierry; Bernier, Eveline

doi:10.1007/978-3-030-29948-4_7

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Elcano [84] implements ISO and OGC-compliant 2D geometry data types, spatial functions, and operators based on Spark SQL [24]. It supports three indexation methods, GeoHash, R-Tree, and a combination of both (hybrid) [83]. However, the authors did not include any information regarding spatial data partitioning and query execution process of Elcano.…”

Section: Spark-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

2022

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Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

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Section: Spark-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

2022

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“…Elcano [71] implements ISO and OGC-compliant 2D geometry data types, spatial functions, and operators based on Spark SQL [16]. It supports three indexation methods, GeoHash, R-Tree, and a combination of both (hybrid) [70]. This paper reports the spatial join performance of Elcano over SpatialSpark and PostgreSQL/PostGIS.…”

Section: Spark-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

Alam¹,

Torgo²,

Bifet³

2021

Preprint

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Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatiotemporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch for processing spatio-temporal data, or by implementing algorithms for mining spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics can be categorized into three groups, (1) spatial databases (SQL and NoSQL), ( 2) big spatio-temporal data processing infrastructures, and (3) programming languages and software tools for processing spatio-temporal data. Since existing surveys mostly investigated big data infrastructures for processing spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics along with an up-to-date review of big spatial data processing systems. This survey also portrays the importance and future of spatial and spatio-temporal data analytics. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Spatialtemporal systems; Parallel and distributed DBMSs.

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Enabling Standard Geospatial Capabilities in Spark for the Efficient Processing of Geospatial Big Data

Cited by 2 publications

References 21 publications

A Survey on Spatio-temporal Data Analytics Systems

A Survey on Spatio-temporal Data Analytics Systems

A Survey on Spatio-temporal Data Analytics Systems

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