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 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.

Please, read the full overview of the tutorial [pdf].

Slides

You can download the final version of the tutorial slides here.

Presenters

Ibrahim Sabek is a PhD candidate at the department of Computer Science and Engineering, University of Minnesota. He received his M.Sc. degree at the same department in 2017. His research interests lie in the intersection area between big spatial data management, spatial computing, and scalable machine learning systems. Ibrahim has been nominated for the Best Paper Award of ACM SIGSPATIAL 2018, and has been qualified to the final stage of ACM SIGMOD Student Research Competition (SRC) 2017. During his PhD, he has collaborated with NEC Labs America, and Microsoft Research (MSR) in Redmond. Ibrahim has published many papers in top research venues, including ACM TSAS, IEEE ICDE, ACM SIGSPATIAL, IEEE TMC, and demonstrated his work at VLDB and ACM SIGMOD.

Mohamed F. Mokbel is the Chief Scientist of Qatar Computing Research Institute and a Professor at University of Minnesota. His current research interests focus on systems and machine learning techniques for big spatial data and applications. His research work has been recognized by the VLDB 10-years Best Paper Award, four conference Best Paper Awards, and the NSF CAREER Award. Mohamed has delivered six tutorials in VLDB/SIGMOD/ICDE/EDBT conferences, in addition to tutorials in other communities’ first-tier venues, including IEEE ICDM and ACM CCS. None of these tutorials overlaps with this tutorial proposal. Mohamed is the past elected Chair of ACM SIGPATIAL, current Editor-in-Chief for Distributed and Parallel Databases Journal, and on the editorial board of ACM Books, ACM TODS, VLDB Journal, ACM TSAS, and GoeInformatica journals. He has also served as PC Vice Chair of ACM SIGMOD and PC CoChair for ACM SIGSPATIAL and IEEE MDM. For more information, please visit: http://www.cs.umn.edu/~mokbel.