Title: Towards Scalable Spatial Data Science
Abstract: The spatial analysis does not lack massive datasets to extract insights and empower various spatial applications. The Google Dataset Search repository indexes 11.3 million datasets from social sciences and geosciences, representing 45.2% of all available datasets. These datasets are dominated by spatial data that requires scalable analysis tools to extract major insights worldwide. Existing popular spatial data science tools neither support large-scale datasets nor make the best use of big data management systems to scale up processing. Existing big spatial data systems are currently not equipped to scale up spatial data science queries as they are optimized for queries of different characteristics. Our work bridges the two worlds through investigating novel query processing techniques. This work is part of a broader vision to identify common utilities for spatial statistical analysis that need to be supported at the system level to enable deep integration between big data systems and spatial data science tools.
Bio: Amr Magdy is an Assistant Professor of Computer Science and Engineering and a co-founding faculty member of the Center for Geospatial Sciences at UC Riverside. His research interests include database systems, spatial data management, big data management, large-scale data analytics, indexing, and main-memory management. His research has been published in prestigious research venues, including VLDB, ACM SIGMOD, ACM SIGSPATIAL, IEEE ICDE, IEEE TKDE, VLDB Journal, and ACM TSAS. Amr’s research is recognized among the best papers in IEEE ICDE 2014 and ACM SIGSPATIAL 2019, and has been incubated by several industrial collaborators. Amr’s research is supported by five NSF awards in different roles with collaborators from UC Riverside, San Diego State University, and the American Association of Geographers (AAG).
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