How Good Are Modern Spatial Libraries?

Pandey, Varun; Renen, Alexander van; Kipf, Andreas; Kemper, Alfons

doi:10.1007/s41019-020-00147-9

Cited by 11 publications

(8 citation statements)

References 46 publications

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“…An example is the PostGIS 8 , a PostgreSQL extension that employs the spatial libraries GEOS and GDAL. There are few studies that compare existing spatial libraries, such as the work in [Pandey et al 2020] that conducts experiments to analyze the performance of spatial libraries when executing spatial queries by using spatial index structures. However, a study that compares the support for spatial approximations is still missing.…”

Section: Discussionmentioning

confidence: 99%

The Application of Spatial Approximations to Spatial Query Processing: A Systematic Review of Literature

Bertella

Lopes

Oliveira

et al. 2021

Anais Do XXXVI Simpósio Brasileiro De Banco De Dados (SBBD 2021)

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Spatial approximations simplify the geometric shape of complex spatial objects. Hence, they have been employed to alleviate the evaluation of costly computational geometric algorithms when processing spatial queries. For instance, spatial index structures employ them to organize spatial objects in tree structures (e.g., the R-tree). We report experiments considering two real datasets composed of ∼1.5 million regions and ∼2.7 million lines. The experiments confirm the performance benefits of spatial approximations and spatial index structures. However, we also identify that a second processing step is needed to deliver the final answer and often requires higher processing time than the step that uses index structures only. It leads to the interest in studying how spatial approximations can be efficiently used to improve both steps. This paper presents a systematic review on this topic. As a result, we provide an overview and comparison of existing approaches that propose, evaluate, or make use of spatial approximations to optimize the performance of spatial queries. Further, we characterize them and discuss some future trends.

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Section: Discussionmentioning

confidence: 99%

The Application of Spatial Approximations to Spatial Query Processing: A Systematic Review of Literature

Bertella

Lopes

Oliveira

et al. 2021

Anais Do XXXVI Simpósio Brasileiro De Banco De Dados (SBBD 2021)

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“…Main-memory servers: Common spatial libraries are limited to in-memory processing and thus require large main-memory servers to handle big data sets. We selected the Google S2 geometry library [17] as a baseline for the performance on large-memory servers, since a recent study reported that it has the most consistent performance across query classes [32]. Furthermore, it is optimized for spherical geometry, and is thus suitable to handle the data sets used in this evaluation.…”

Section: Methodsmentioning

confidence: 99%

“…Pandey et al [31] presented a detailed experimental evaluation of the state-of-the-art cluster-based spatial analytics systems. Pandey et al [32] also evaluated common spatial libraries. As we discussed, these approaches are effective but out of reach for many analysts.…”

Section: Related Workmentioning

confidence: 99%

GPU-Powered Spatial Database Engine for Commodity Hardware: Extended Version

Doraiswamy¹,

Freire²

2022

Preprint

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Given the massive growth in the volume of spatial data, there is a great need for systems that can efficiently evaluate spatial queries over large data sets. These queries are notoriously expensive using traditional database solutions. While faster response times can be attained through powerful clusters or servers with large main-memory, these options, due to cost and complexity, are out of reach to many data scientists and analysts making up the long tail.Graphics Processing Units (GPUs), which are now widely available even in commodity desktops and laptops, provide a cost-effective alternative to support high-performance computing, opening up new opportunities to the efficient evaluation of spatial queries. While GPU-based approaches proposed in the literature have shown great improvements in performance, they are tied to specific GPU hardware and only handle specific queries over fixed geometry types.In this paper we present SPADE, a GPU-powered spatial database engine that supports a rich set of spatial queries. We discuss the challenges involved in attaining efficient query evaluation over large datasets as well as portability across different GPU hardware, and how these are addressed in SPADE. We performed a detailed experimental evaluation to assess the effectiveness of the system for wide range of queries and datasets, and report results which show that SPADE is scalable and able to handle data larger than main-memory, and its performance on a laptop is on par with that other systems that require clusters or large-memory servers.

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“…Even libraries and packages of Python or R have also utilized the GEOS library for modeling spatial data. Recently, researchers have also proposed benchmarks [188,281] for computational geometry libraries used in data processing systems for spatial data exploration.…”

Section: Programming and Software Tools For Spatio-temporal Analysismentioning

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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How Good Are Modern Spatial Libraries?

Cited by 11 publications

References 46 publications

The Application of Spatial Approximations to Spatial Query Processing: A Systematic Review of Literature

The Application of Spatial Approximations to Spatial Query Processing: A Systematic Review of Literature

GPU-Powered Spatial Database Engine for Commodity Hardware: Extended Version

A Survey on Spatio-temporal Data Analytics Systems

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