The proliferation in amounts of generated data has propelled the rise of scalable machine learning solutions to efficiently analyze and extract useful insights from such data. Meanwhile, spatial data has become ubiquitous, e.g., GPS data, with increasingly sheer sizes in recent years. The applications of big spatial data span a wide spectrum of interests including tracking infectious disease, climate change simulation, drug addiction, among others. Consequently, major research efforts are exerted to support efficient analysis and intelligence inside these applications by either providing spatial extensions to existing machine learning solutions or building new solutions from scratch. In this 90-minutes tutorial, we comprehensively review the state-of-the-art work in the intersection of machine learning and big spatial data. We cover existing research efforts and challenges in three major areas of machine learning, namely, data analysis, deep learning and statistical inference. We also discuss the existing end-to-end systems, and highlight open problems and challenges for future research in this area.
|Original language||English (US)|
|Title of host publication||Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|State||Published - Apr 2020|
|Event||36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States|
Duration: Apr 20 2020 → Apr 24 2020
|Name||Proceedings - International Conference on Data Engineering|
|Conference||36th IEEE International Conference on Data Engineering, ICDE 2020|
|Period||4/20/20 → 4/24/20|
Bibliographical noteFunding Information:
∗Also affiliated with University of Minnesota, MN, USA. 1This work is partially supported by the National Science Foundation, USA, under Grants IIS-1907855, IIS-1525953 and CNS-1512877.
© 2020 IEEE.