A Survey on Spatio-temporal Data Analytics Systems

Alam, Mahbub; Torgo, Luı́s; Bifet, Albert

doi:10.1145/3507904

Cited by 37 publications

(16 citation statements)

References 145 publications

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“…It classified the spatio temporal data into five types: event, trajectory, point reference, raster, and video. Alam et al (2021) classified the STD analytics systems into three categories and provided definitions and related applications for STD. Moreover, it conducted investigations and discussions on existing programming languages, development tools, and data platforms.…”

Section: Introductionmentioning

confidence: 99%

Deep learning on multi-view sequential data: a survey

et al. 2022

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With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions.

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Section: Introductionmentioning

confidence: 99%

Deep learning on multi-view sequential data: a survey

et al. 2022

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“…Multiple GIS tools and geospatial database management systems support georeferencing and analytics 19,20 . PostgreSQL, 21 MySQL, 22 and MongoDB 23 include support for geospatial relationship queries to evaluate whether a geospatial object (e.g., point or polygon) lies inside the spatial bounds of a given query.…”

Section: Related Workmentioning

confidence: 99%

“…Multiple GIS tools and geospatial database management systems support georeferencing and analytics. 19,20 PostgreSQL, 21 MySQL, 22 and Mon-goDB 23 include support for geospatial relationship queries to evaluate whether a geospatial object (e.g., point or polygon) lies inside the spatial bounds of a given query. ArcGIS 24 is a geographic information system designed for working with geospatialspatial data and imagery and managing geographic information in a database, providing a wide range of geospatial analytics and mapping capabilities for geospatial data but it does not scale well for large-scale raw sensor data.…”

Section: Related Workmentioning

confidence: 99%

Radix+: High‐throughput georeferencing and data ingestion over voluminous and fast‐evolving phenotyping sensor data

Mitra

Roselius²,

Andrade-Sánchez

et al. 2023

Concurrency and Computation

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Remote sensing of plant traits and their environment facilitates non-invasive, high-throughput monitoring of the plant's physiological characteristics. However, voluminous observational data generated by such autonomous sensor networks overwhelms scientific users when they have to analyze the data. In order to provide a scalable and effective analysis environment, there is a need for storage and analytics that support high-throughput data ingestion while preserving spatiotemporal and sensor-specific characteristics. Also, the framework should enable modelers and scientists to run their analytics while coping with the fast and continuously evolving nature of the dataset. In this paper, we present RADIX+, a high-throughput distributed data storage system for supporting scalable georeferencing, and interactive query-based spatiotemporal analytics with trackable data integrity. We include empirical evaluations performed on a commodity machine cluster with up to 1 TB of data. Our benchmarks demonstrate subsecond latency for majority of our evaluated queries and ∼ 8× improvement in data ingestion rate over systems such as Geomesa.

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“…Due to the importance of spatial joins in spatial database systems and GIS, other surveys [Jacox and Samet 2007;Bouros and Mamoulis 2019] concentrate on describing specific algorithms that possibly use spatial index structures to answer this type of spatial query. Other surveys are dedicated to describing spatial index structures applied to modern storage devices [Fevgas et al 2020], spatial analytics systems based on Hadoop and Spark [Castro et al 2020], and existing ecosystems for spatial data handling [Alam et al 2021].…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Spatial Approximations in Spatial Database Systems

Bertella

Lopes

Oliveira

et al. 2022

jidm

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Many applications rely on spatial information retrieval, which involves costly computational geometric algorithms to process spatial queries. Spatial approximations simplify the geometric shape of complex spatial objects, allowing faster spatial queries at the expense of result accuracy. In this sense, spatial approximations have been employed to efficiently reduce the number of objects under consideration, followed by a refinement step to restore accuracy. For instance, spatial index structures employ spatial approximations to organize spatial objects in hierarchical structures (e.g., the R-tree). It leads to the interest in studying how spatial approximations can be efficiently employed to improve spatial query processing. This article presents a systematic review on this topic. We gather relevant studies by performing a search string on several digital libraries. We further expand the studies under consideration by employing a single iteration of the snowballing approach, where we track the reference list of selected papers. 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. The spatial approximations mentioned by the approaches are also summarized. Further, we characterize the approaches and discuss some future trends.

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A Survey on Spatio-temporal Data Analytics Systems

Cited by 37 publications

References 145 publications

Deep learning on multi-view sequential data: a survey

Deep learning on multi-view sequential data: a survey

Radix+: High‐throughput georeferencing and data ingestion over voluminous and fast‐evolving phenotyping sensor data

A Systematic Review of Spatial Approximations in Spatial Database Systems

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