Machine Learning Meets Big Spatial Data (Revised)

Ibrahim Sabek, Mohamed F. Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 languageEnglish (US)
Title of host publicationProceedings - 2021 22nd IEEE International Conference on Mobile Data Management, MDM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-8
Number of pages4
ISBN (Electronic)9781665428453
DOIs
StatePublished - Jun 1 2021
Event22nd IEEE International Conference on Mobile Data Management, MDM 2021 - Virtual, Online
Duration: Jun 15 2021Jun 18 2021

Publication series

Name2021 22nd IEEE International Conference on Mobile Data Management (MDM)

Conference

Conference22nd IEEE International Conference on Mobile Data Management, MDM 2021
CityVirtual, Online
Period6/15/216/18/21

Bibliographical note

Funding Information:
1This work is partially supported by the National Science Foundation, USA, under Grants IIS-1907855, IIS-1525953, CNS-1512877 and CCF-2030859.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Big data
  • Machine Learning
  • Spatial data

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