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

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dc.contributor.authorJitkajornwanich, Kulsawasd
dc.contributor.authorPant, Neelabh
dc.contributor.authorFouladgar, Mohammadhani
dc.contributor.authorElmasri, Ramez
dc.date.accessioned2026-08-06T10:26:42Z
dc.date.available2026-08-06T10:26:42Z
dc.date.issued2020-01-01
dc.description.abstractSpatio-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.
dc.identifier.citationJournal of Information and Telecommunication, 4(4), 524-559, 2020
dc.identifier.doi10.1080/24751839.2020.1774153
dc.identifier.issn24751839
dc.identifier.other2-s2.0-85104897396
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10451
dc.sourceJournal of Information and Telecommunication
dc.subjectDeep learning
dc.subjectRainfall analysis
dc.subjectSpatio-temporal database
dc.subjectSQL
dc.subjectSurvey
dc.titleA survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using sql ecosystem and deep learning
dc.typeArticle

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