Abstract
Management of big spatio-temporal data such as the results from large scale global climate models has long been a challenge because of the sheer vastness of the dataset. Although different data management systems like that incorporate a relational database management system have been proposed and widely used in prior studies, solutions that are particularly designed for big spatio-temporal data management have not been studied well. In this paper, we propose a general data management platform for high-dimensional spatio-temporal datasets like those found in the climate domain, where different database systems can be applied. Through this platform, we compare and evaluate several database systems including SQL database and NoSQL database from various aspects and explore the key impact factors for system performance. Our experimental results indicate advantages and disadvantages of each database system and give insight into the best system to use for big spatio-temporal data applications. Our analysis provides important insights into the understanding of performance of different data management systems, which is very useful for designing high dimensional big data applications.
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Lian, J., Miao, S., McGuire, M., Tang, Z. (2018). SQL or NoSQL? Which Is the Best Choice for Storing Big Spatio-Temporal Climate Data?. In: Woo, C., Lu, J., Li, Z., Ling, T., Li, G., Lee, M. (eds) Advances in Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11158. Springer, Cham. https://doi.org/10.1007/978-3-030-01391-2_32
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DOI: https://doi.org/10.1007/978-3-030-01391-2_32
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