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 seminar, 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 - 2021 22nd IEEE International Conference on Mobile Data Management, MDM 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - Jun 1 2021|
|Event||22nd IEEE International Conference on Mobile Data Management, MDM 2021 - Virtual, Online|
Duration: Jun 15 2021 → Jun 18 2021
|Name||2021 22nd IEEE International Conference on Mobile Data Management (MDM)|
|Conference||22nd IEEE International Conference on Mobile Data Management, MDM 2021|
|Period||6/15/21 → 6/18/21|
Bibliographical noteFunding Information:
1This work is partially supported by the National Science Foundation, USA, under Grants IIS-1907855, IIS-1525953, CNS-1512877 and CCF-2030859.
© 2021 IEEE.
- Big data
- Machine Learning
- Spatial data