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Seminar: Machine Learning for Big Spatial Data and Applications

Publié le 22 avril 2024 Mis à jour le 14 mai 2024

Biography: Mohamed Mokbel is a Distinguished McKnight University Professor at the University of Minnesota. Prior roles while on leave/sabbatical from UMN include Chief Scientist of Qatar Computing Research Institute, Founding Technical Director of GIS Technology Innovation Center in Saudi Arabia, and multiple times Visiting Researcher at Microsoft Research, USA. His research interests include database systems, spatial data, and GIS. His research work has been recognized by the NSF CAREER Award, ACM SIGSPATIAL 10-Year Impact Award, VLDB 10-years Best Paper Award, and four conference Best Paper Awards. Mohamed is the past elected Chair of ACM SIGSPATIAL, current Editor-in-Chief for Springer Distributed and Parallel Databases Journal, and on the editorial board of ACM Books, ACM TODS, VLDB Journal, ACM TSAS, and GeoInformatica journals. He has served as PC Co-Chair for ACM SIGMOD, ACM SIGSPATIAL, and IEEE MDM. Mohamed is an IEEE Fellow and ACM Distinguished Scientist.

Abstract: This talk will focus on our recent efforts in adopting machine learning (ML) techniques for big spatial data and applications. This includes going for two orthogonal, but related, directions. In the first direction, we show that traditional ML-based applications like knowledge-base construction and data cleaning are missing a great opportunity by not incorporating the distinguishing characteristics of spatial data in their core operations. We then show that injecting spatial-awareness into the core ML operations behind these applications significantly boost their accuracy. In the second direction, we show that traditional spatial applications can benefit from the recent advances in ML techniques to significantly boost their scalability and accuracy. We will focus on two main widely used spatial applications, namely, map services (e.g., shortest path queries), and trajectory data management.
Le 13 mai 2024

de 14h à 16h

Campus du Solbosch

S. UB4.132