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- W2956618284 abstract "The conventional studies of spatio-temporal data models and their big data applications cannot reliably reflect the large volume, heterogeneity and dynamics of spatio-temporal big data. In this paper, the structure and function expression of spatio-temporal metadata is analyzed. With fused and normalized spatio-temporal reference and data structure, the constraint rules of spatio-temporal big data refinement are proposed. Using the domain specific modeling (DSM) and the data granulation algorithms, an object-oriented modeling language, the thrust modeling of spatio-temporal big data, and the aggregated status correlation of unified model data are established. This work utilizes the trust modeling theory and the spatio-temporal data processing methods and defines a case study that converts spatio-temporal data into dynamic complex big data. This research paves the way for the trust modeling and validation of spatio-temporal big data." @default.
- W2956618284 created "2019-07-23" @default.
- W2956618284 creator A5071798264 @default.
- W2956618284 date "2019-01-01" @default.
- W2956618284 modified "2023-09-23" @default.
- W2956618284 title "Refinement and Trust Modeling of Spatio-Temporal Big Data" @default.
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- W2956618284 doi "https://doi.org/10.1007/978-3-030-22868-2_10" @default.
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