Big Spatial Data: NoSQL and NewSQL Data Stores.Big Spatial Data: Modern Hardware, High Performance Computing, and Cloud.Big Spatial Data: Privacy and Authentication.Big Spatial Data: Descriptive, Predictive, and Prescriptive Models.Big Spatial Data: Spatial Graph Processing.
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Big Spatial Data: Spatial Time Series Querying and Mining.Big Spatial Data: Fundamentals and Theory.We encourage researchers from academia and industry to submit papers that highlight the value of Big Spatial Data processing, management, mining, and analysis on topics that include, but are not limited to the following This workshop offers the audience the opportunity to discuss the lessons which they have learned over the years, to demonstrate what they have achieved so far, and to plan for the future of “Big Spatial Data”. The aim of this workshop is to bring together researchers and stakeholders from academia, government, and industry who are encountering and actively addressing problems in the area of Big Spatial Data management and analysis. Turning Big Spatial Data into value is challenging and requires introduction of fundamentally new spatio-temporal algorithms, methods, and systems that can store, mine, and analyze massive amounts of fast growing and heterogeneous spatio-temporal data in a timely manner. The use of Big Spatial Data spans a variety of applications including social networks, earth sciences, transportation, communication networks, online maps, smart cities and urban planning, remote sensing, and crisis and evacuation management, to name but a few. Thus, “Big Spatial Data” deals with massive amounts of real-time spatial and spatio-temporal data obtained from billions of sensors, location-aware devices, remoting sensing satellites, and various models of the physical world. Geoimages, (b) Vector data, e.g., Points, Lines, Polygons, and (c) Graph data, e.g. Big Spatial Data covers a wide spectrum of data types including (a) Raster data, e.g.
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