In this seminar, we address spatial predictive queries both in Euclidian spaces and over road networks. We provide a definition for various types of spatial predictive queries, describe current research trends, and envision future directions. We present practical application scenarios and emphasize the roadblocks that are holding industry back from the commercialization of spatial predictive queries. This seminar targets audience in mobile data management, spatiotemporal query processing, mobile crowd sourcing, and tracking of moving objects.
Abdeltawab Hendawi is a research associate at the Center for Data Science, University of Washington, Tacoma and a PhD candidate at the Department of Computer Science and Engi- neering at the University of Minnesota. Abdeltawab’s research interests are centred around database systems, big-data mining, spatio-temporal data management, and volunteered geographic information systems. His PhD focuses on predictive query processing against moving objects. Abdeltawab built the iRoad system for predictive queries on road networks, the PANDA system for predictive queries in the Euclidean space, and the iTornado system for predicting the spatio-temporal behavior of severe weather conditions. Prior to joining the University of Minnesota, Abdeltawab obtained his B.Sc. and M.Sc. degrees in Computer Science from Cairo University in Egypt.
Mohamed Ali is an associate professor at the Institute of Technology, University of Washington, Tacoma. Mohamed’s research interests include the processing, analysis and visual- ization of data streams with geographic and spatial informa- tion. For the past decade, Mohamed has been building com- mercial spatiotemporal data streaming systems to cope with the emerging Big Data requirements. In 2006, Mohamed and his colleagues at the database group at Microsoft Research ramped up the Complex Event Detection and Response (CEDR) project. Then, Mohamed joined the SQL Server group at Microsoft to productize the CEDR project. CEDR has shipped and brand-named as Microsoft StreamInsight. Since the first public release of StreamInsight, Mohamed has been advocating for real-time spatiotemporal data management everywhere; that is the use of StreamInsight in monitoring, managing and mining real time geospatial information across a diversity of verticals. These verticals include but are not limited to: online advertising, behavioral targeting, business intelligence, computational finance, traffic management, social networking, homeland security, emergency and crisis management. In 2011, Mohamed started another journey at Microsoft Bing Maps where he became at the frontline with the Big Data challenge and where he battled various types of spatial search queries. In 2014, Mohamed joined the University of Washington, Tacoma where he leads the geospatial data science team at the Center for Data Science.