A Survey on Spatio-Temporal Big Data Analytics Ecosystem: Resource Management, Processing Platform, and Applications

• Published in: IEEE transactions on big data 2024-04, Vol.10 (2), p.174-193
• Main Authors: Liang, Huanghuang, Zhang, Zheng, Hu, Chuang, Gong, Yili, Cheng, Dazhao
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Artificial intelligence framework; Big Data; Big Data system; Cloud computing; cloud platform; Data analysis; Data management; Data processing; database management system; Ecosystems; geographic information system; Geographic information systems; Internet of Things; Mathematical analysis; Modules; Resource management; spatio-temporal Big Data; Spatiotemporal data
• ISSN: 2332-7790; 2372-2096
• DOI: 10.1109/TBDATA.2023.3342619

With the rapid evolution of the Internet, Internet of Things (IoT), and geographic information systems (GIS), spatio-temporal Big Data (STBD) is experiencing exponential growth, marking the onset of the STBD era. Recent studies have concentrated on developing algorithms and techniques for the collection, management, storage, processing, analysis, and visualization of STBD. Researchers have made significant advancements by enhancing STBD handling techniques, creating novel systems, and integrating spatio-temporal support into existing systems. However, these studies often neglect resource management and system optimization, crucial factors for enhancing the efficiency of STBD processing and applications. Additionally, the transition of STBD to the innovative Cloud-Edge-End unified computing system needs to be noticed. In this survey, we comprehensively explore the entire ecosystem of STBD analytics systems. We delineate the STBD analytics ecosystem and categorize the technologies used to process GIS data into five modules: STBD, computation resources, processing platform, resource management, and applications. Specifically, we subdivide STBD and its applications into geoscience-oriented and human-social activity-oriented. Within the processing platform module, we further categorize it into the data management layer (DBMS-GIS), data processing layer (BigData-GIS), data analysis layer (AI-GIS), and cloud native layer (Cloud-GIS). The resource management module and each layer in the processing platform are classified into three categories: task-oriented, resource-oriented, and cloud-based. Finally, we propose research agendas for potential future developments.