![]() IEEE Transactions on Intelligent Transportation Systems, 2016, 17(7): 1 816-1 825 ![]() Matrix and Tensor Based Methods for Missing Data Estimation in Large Traffic Networks. ![]() Knowledge-Based Systems, 2017, 132: 249-262 Īsif MT, Mitrovic N, Dauwels J, et al. Ensemble Correlation-based Low-rank Matrix Completion with Applications to Traffic Data Imputation. ISPRS International Journal of Geo-Information, 2016, 5(2): 13 Ĭhen X, Wei Z, Li Z, et al. A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets. Acta Geographica Sinica, 2014, 69(9): 1 326-1 345 Ĭheng T, Haworth J, Anbaroglu B, et al. Spatiotemporal Data Analysis in Geography. ISPRS International Journal of Geo-Information, 2015, 4(4): 2 306-2 338 Spatiotemporal Data Mining: A Computational Perspective. Spatio-Temporal Data Mining: A Survey of Problems and Methods. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(8): 1 544-1 554 Ītluri G, Karpatne A, Kumar V. Machine Learning for the Geosciences: Challenges and Opportunities. Karpatne A, Ebert-Uphoff I, Ravela S, et al. The Intelligent Processing and Service of Spatiotemporal Big Data. Acta Geodaetica et Cartographica Sinica, 2016, 45(4): 379-384 Towards Geo-Spatial Information Science in Big Data Era. Zhou Chenghu, Zhu Xinyan, Wang Meng, et al. The proposed methods are expected to enrich the method system in the field of spatiotemporal data mining and improve the quality and application value of spatiotemporal data modeling. We systematically summarized the research status and existing problems of several key spatiotemporal mining tasks including missing spatiotemporal data interpolation, sparse spatiotemporal data reconstruction, and spatiotemporal state prediction, condensed four key scientific problems, and gave four corresponding solutions. Thus, this paper focuses on the series of bottlenecks faced in the expression and application of heterogeneous and sparsely distributed spatiotemporal data. However, the universal heterogeneity and sparse distribution characteristics of spatiotemporal big data restrict the realization of spatiotemporal data mining algorithms, and significantly affect the description and analysis capabilities of natural and social complex systems. In the era of big data, the explosive growth of geographic spatiotemporal data puts forward an urgent demand for spatiotemporal knowledge discovery, which promotes the continuous development of spatiotemporal data mining technology. Spatiotemporal data mining is the core research topic of geographic information science. ![]()
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