TL;DR
This survey comprehensively reviews the ecosystem of spatial and spatio-temporal data analytics, including databases, processing infrastructures, and tools, highlighting recent developments and future directions in the field.
Contribution
It provides an up-to-date, holistic overview of the entire spatio-temporal data analytics ecosystem, which was lacking in previous surveys focused mainly on big data infrastructures.
Findings
Categorizes spatio-temporal data analytics systems into three main groups.
Highlights recent advancements in spatial databases, processing infrastructures, and tools.
Discusses future trends and importance of spatio-temporal data analytics.
Abstract
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 for processing spatio-temporal data, or by implementing algorithms for mining spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics can be categorized into three groups, (1) spatial databases (SQL and NoSQL),…
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