A Survey of Spatio-Temporal Database Research

Pant, Neelabh; Fouladgar, Mohammadhani; Elmasri, Ramez; Jitkajornwanich, Kulsawasd

doi:10.1007/978-3-319-75420-8_11

Cited by 10 publications

(15 citation statements)

References 29 publications

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“…An example of how spatial data can be represented differently using these two models as well as their respective operators that are supported w.r.t. OGC (Open Geospatial Consortium) is shown in Figure 1 (Pant et al, 2018).…”

Section: Spatial Database Researchmentioning

confidence: 99%

“…The selection as to which R-tree variation to use depends on the characteristics (such as size, shape, and distribution) of spatial objects as well as the spatial types will be invoked (Neelabh et al, 2016). Example of how spatial data can be represented and operated using spatial data models (Pant et al, 2018).…”

Section: Spatial Indexingmentioning

confidence: 99%

“…In this subsection, spatial and temporal aspects of data are combined to better model spatiotemporal properties of databases (see Figure 3 (Pant et al, 2018)). Research studies surveyed in this subsection include spatio-temporal modelling, query languages, and operators; multivariable, multi-dimensional data, and concept hierarchies; and spatio-temporal indexing.…”

Section: Spatio-temporal Database Researchmentioning

confidence: 99%

“…Four types of spatial objects (i.e. point, linestring, polygon, and collection of geometry/geography) are mainly used in spatio-temporal query where the existence, position, and shape of the objects are measured (see Figure 3 for sample spatio-temporal queries (Pant et al, 2018)). Most location-based services, esp.…”

Section: Spatio-temporal Modelling Query Languages and Operatorsmentioning

confidence: 99%

See 3 more Smart Citations

A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using SQL ecosystem and deep learning

Jitkajornwanich

Pant

Fouladgar

et al. 2020

Journal of Information and Telecommunication

Self Cite

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Spatio-temporal data serves as a foundation for most location-based applications nowadays. To handle spatio-temporal data, an appropriate methodology needs to be properly followed, in which space and time dimensions of data must be taken into account 'altogether'unlike spatial (or temporal) data management tools which consider space (or time) separately and assumes no dependency on one another. In this paper, we conducted a survey on spatial, temporal, and spatio-temporal database research. Additionally, to use an original example to illustrate how today's technologies can be used to handle spatio-temporal data and applications, we categorize the current technologies into two groups: (1) traditional, mainstay tools (e.g. SQL ecosystem) and (2) emerging, data-intensive tools (e.g. deep learning). Specifically, in the first group, we use our spatio-temporal application based on SQL system, 'hydrological rainstorm analysis', as an original example showing how analysis and mining tasks can be performed on the conceptual storm stored in a spatio-temporal RDB. In the second group, we use our spatio-temporal application based on deep learning, 'users' future locations prediction based on historical trajectory GPS data using hyper optimized ANNs and LSTMs', as an original example showing how deep learning models can be applied to spatio-temporal data.

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Section: Spatial Database Researchmentioning

confidence: 99%

Section: Spatial Indexingmentioning

confidence: 99%

Section: Spatio-temporal Database Researchmentioning

confidence: 99%

Section: Spatio-temporal Modelling Query Languages and Operatorsmentioning

confidence: 99%

See 2 more Smart Citations

A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using SQL ecosystem and deep learning

Jitkajornwanich

Pant

Fouladgar

et al. 2020

Journal of Information and Telecommunication

Self Cite

View full text Add to dashboard Cite

show abstract

“…In prevalent geographic information systems (GIS), the environment is conventionally represented as 'flat layers' with a timestamp denoting when the data were collected, created, and/or published. Some geodatabases can store temporal information of spatial features as attributes in relational tables to support time-related queries and analyses [1,2]. However, the capacity of temporal analysis in such geodatabases is rather limited compared to the extensive toolbox for spatial data analyses in GIS.…”

Section: Introductionmentioning

confidence: 99%

Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses

Qiang

Weghe

2019

IJGI

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The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data.

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Using Industry-based Spatio-Temporal Database to Store and Retrieve Big Trafc Data

Goldner,

Erdelić,

Carić

et al. 2023

Transportation Research Procedia

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A Survey of Spatio-Temporal Database Research

Cited by 10 publications

References 29 publications

A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using SQL ecosystem and deep learning

A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using SQL ecosystem and deep learning

Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses

Using Industry-based Spatio-Temporal Database to Store and Retrieve Big Trafc Data

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