Big Spatial Data covers a wide spectrum of data types including (a) Raster data, e.g. Geoimages, (b) Vector data, e.g., Points, Lines, Polygons, and (c) Graph data, e.g. Road network graph. 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. 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. Turning Big Spatial Data into value is challenging and requires introduction of fundamentally new spatio-temporal algorithms, methods, and systems that can process, mine, and analyze massive amounts of fast and heterogeneous spatiotemporal data in a timely manner.
The aim of this workshop is to bring together researchers from academia, government and industry who are actively addressing problems in the area of Big Spatial Data management and analysis. 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”.
Call for Papers
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:
- ● Big Spatial Data: Fundamentals and Theory
- ● Big Spatial Data: Management
- ● Big Spatial Data: Mining and Analysis
- ● Big Spatial Data: Stream Processing
- ● Big Spatial Data: Spatial Time Series Querying and Mining
- ● Big Spatial Data: Spatial Graph Processing
- ● Big Spatial Data: Descriptive, Predictive, and Prescriptive Models
- ● Big Spatial Data: Privacy and Authentication
- ● Big Spatial Data: Geosensing
- ● Big Spatial Data: Indexing
- ● Big Spatial Data: Modern Hardware, High Performance Computing, and Cloud
- ● Big Spatial Data: Visualization
- ● Big Spatial Data: Deep Learning
- ● Big Spatial Data: NoSQL and NewSQL Data Stores
- ● Big Spatial Data: Data Frameworks
- ● Big Geospatial Information Retrieval and Crowdsourcing
- ● Big Geosocial Networks
- ● Big Spatial Data Applications: Smart Cities and Intelligent Transportation, Urban Computing, Healthcare, Geosciences, etc.
Paper Submission Deadline: Oct 1, 2019
Notification of Acceptance: Nov 1, 2019
Camera-Ready Submissions: Nov 15, 2019
Workshop Date: December 9-12, 2019
Authors with interests in any of the listed topics or any other topic related to Big Spatial Data paradigm are cordially invited to submit their work. All submissions should be in high quality, original and not published or under review elsewhere during the review process.
Submitted papers have to follow the IEEE official template. Maximum paper length allowed is:
Full Papers: 10 pages
Short (work-in-progress) Papers: 4 pages
Demo Papers: 4 pages
Position/Vision Papers: 4 pages
If from last three categoizes listed above, the title of the paper should start with “Short Paper:”, “Demo Paper:”, or “Vision Paper:”, respectively. Submitted papers will be reviewed by members of the Workshop Program Committee. At least one author for each accepted paper has to register and present the work.
Paper submission website: click here!
See 2019 IEEE Big Data Conference Registration Page.
See 2019 IEEE Big Data Conference Accommodations Page.
Farnoush Banaei-Kashani, email@example.com, University of Colorado Denver
Farnoush Banaei-Kashani is currently an assistant professor at the Department of Computer Science and Engineering, College of Engineering and Applied Science, University of Colorado Denver. Previously, he was a research scientist at the Computer Science Department, University of Southern California (USC), where he also earned his PhD degree in Computer Science and MS degree in Electrical Engineering in 2007 and 2002, respectively. Dr. Banaei-Kashani is passionate about performing fundamental research toward building practical, large-scale data-intensive systems, with particular interest in Data-driven Decision-making Systems (DDSs), i.e., systems that automate the process of decision-making based on (big) data. In the past he has introduced a number of novel DDSs in various application areas (including, transportation, health, safety and security, energy, and scientific computing). Dr. Banaei-Kashani has published more than 65 referred papers and has received several awards. His research has been supported by grants from both governmental agencies (NSF/CENS, NIH/CTSI, DOT/METRANS, DOJ/NIJ and NASA/JPL) and industry (Google, IBM, Chevron and NGC).
Siyuan Lu, firstname.lastname@example.org, Amazon
Siyuan Lu is currently a Manager and Principal Research Staff Member leading the Data Intensive Physical Analytics group at the IBM T. J. Watson Research Center, Yorktown Heights, NY. He received the Ph.D. degree in Physics from University of Southern California (USC) in 2006. Prior to joining IBM in 2012, he was an Assistant Research Professor jointly appointed in the Department of Physics and the Department of Ophthalmology at USC. Dr. Lu’s current research interests at IBM include architectures of geospatial data services and the union of physics and data-driven approaches for modeling complex systems with applications in renewable energy forecasting, climate forecasting, and environmental monitoring. He technically leads the Department of Energy funded project on solar energy forecasting and the development of IBM PAIRS geospatial Big Data services. He has co-authored over 50 peer-reviewed articles and has served as members on multiple governmental committees.
Chengyang Zhang, email@example.com, Amazon
is currently a Software Development Manager at Amazon.com leading a team to build end to end Machine Learning system for Amazon’s retail pricing systems. He completed his PhD in Computer Science from the University of North Texas in 2011. Dr. Zhang is actively working on various projects related to database query optimization and big data systems. His research interests include data warehousing, Internet of Things, big data systems, geo-streaming data processing, spatial databases and data mining. Dr. Zhang has published over 20 peer reviewed papers and owns two patents.
Abdeltawab Hendawi, firstname.lastname@example.org, University of Rhode Island
is currently an Assistant Professor Computer Science at the University of Rhode Island. He is also the Co-Director of the AI at URI. He obtained his MSc and PhD in Computer Science and Engineering from the University of Minnesota, Twin Cities. His research interests are centered on the broad area of Data Science (big data management and analytics) with more focus on smart cities and smart health related applications. His work has been recognized by a number of awards, including the Best Paper Award at ACM SIGSPATIAL MobiGIS 2012; Best Design/Plan Poster Award and Best Overall Poster Award at the U-Spatial Symposium 2013; Best Demo Paper Award at ACM SIGSPATIAL 2014; Data Science Research Fellowship at the University of Washington 2014; Best and Second Best Demo Paper Awards at IEEE MDM 2015; Hobby Postdoc Research Fellowship at the University of Virginia 2015; Blue Sky Ideas Award 2016 and Best Runner-up Vision Paper Award at ACM SIGSPATIAL 2016; Best Poster Award at the UVa Research Symposium 2018; and Best Demo Paper Award Runner-up at ACM SIGSPATIAL 2018.
● Fusheng Wang, Stony Brook University
● Yan Huang, University of North Texas
● Ahmed Eldawy, University of California Riverside
● Dave Oliver, ESRI
● Amr Magdy, University of California, Riverside
● Jianting Zhang, City College of New York and CUNY Graduate Center
● Mohamed Khalifa, Alexandria University
● Xiao Liu, University of Arkansas
● Mahdi Boukhechba, University of Virginia
● Demetris Zeinalipour, University of Cyprus
● Ayman Taha, Technological University Dublin
● Shahab Helmi, email@example.com