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

Abstract

ABSTRACT 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.