Machine Learning for Big Spatial Data and Applications

Speaker:  Mohamed F Mokbel – Shoreview, USA
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

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.

About this Lecture

Number of Slides:  0
Duration:  60 minutes
Languages Available:  English
Last Updated:  20/03/2026

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