Everyday Big Spatial Data is generated in massive amounts from sensors, location-aware devices, remoting sensing satellites, and various real-time and spatio-temporal models of the physical world. This data is available in different formats such as Raster data, e.g., Geoimages, Vector data, e.g., Points, Lines, Polygons, and Graph data, e.g., Road network graph. It is used in a wide 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 a few. However, to be able to deliver insights and extract value requires 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 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. 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 workshop will be organized fully online.
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: Privacy and Authentication
- ● Big Spatial Data: Descriptive, Predictive, and Prescriptive Models
- ● 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: October 1, 2022
Notification of Acceptance: November 1, 2022
Camera-Ready Submissions: November 20, 2022
Workshop Date: December 17-20, 2022
Submission Guidelines and Instructions
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: Submit.
Invited Keynote Speakers
As we did in previous years of this workshop, we plan to include a keynote and a panel in the workshop program. Details of the plans will be finalized upon acceptance of the workshop proposal.
Farnoush Banaei-Kashani, firstname.lastname@example.org, 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).
Chengyang Zhang, email@example.com, Amazon
Chengyang Zhang is currently a Software Development Manager at Amazon.com leading a team across retail and AWS on PartiQL - A SQL-compatible access to relational, semi-structured, and nested data. 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 and machine learning systems. His research interests include query processing, spatial databases and bridging database with ML systems. Dr. Zhang has published over 20 peer reviewed papers and owns 5 patents.
Abdeltawab Hendawi, firstname.lastname@example.org, University of Rhode Island
Abdeltawab Hendawi is currently an Assistant Professor in the Department of Computer Science and Statistics at the University of Rhode Island, and the Co-director of the AI-Lab at URI. He obtained his MSc and PhD in Computer Science and Engineering from the University of Minnesota, Twin Cities. His research interests are in big data management and analytics with applications in smart city and smart health. Dr. Hendawi has won a number of awards, including the Best Paper Award at the ACM SIGSPATIAL MobiGIS 2012, the Best Design/Plan Poster Award at the U-Spatial Symposium 2013; Best Overall Poster Award, also at the U-Spatial Symposium 2013; the Best Demo Paper Award at the ACM SIGSPATIAL 2014; and Best and Second-Best Demo Paper Awards at the IEEE Mobile Data Management (MDM) 2015. My vision for the future research challenges in the area of location-aware services also won a highly recognized Award at the ACM SIGSPATIAL 2016. In addition, he is the recipient of the Hobby Postdoctoral Research Fellowship 2015-2018 from the Computer Science Department at the University of Virginia, and the Best Poster Award at the UVa Research Symposium 2018; and Best Demo Paper Award Runner-up at ACM SIGSPATIAL 2018. He has been a co-chair for the IEEE Big Spatial Data workshop (BSD) 2016 to 2022.
Ashwin Shashidharan , AShashidharan@esri.com, Esri
Ashwin Shashidharan is currently a Software Developer at Esri with the GeoAnalytics team, where he actively works on developing scalable algorithms to analyze large spatiotemporal datasets. He holds MS and PhD degrees in Computer Science from North Carolina State University. His research interests include geospatial simulation, big spatial data management and distributed spatiotemporal analytics. Dr. Shashidharan has won a number of awards, including the Student Research Competition Award at the ACM SIGSPATIAL SRC 2016, and the Esri EDC International Student Award 2018. He has co-authored over 10 peer-reviewed papers, served on multiple review committees (BDAC, SSTD, SSTDM, ANNSIM, GeoSim, BigSpatial, SIGSPATIAL) and has served as co-chair for the ACM SIGSPATIAL BigSpatial workshop (2019-2020) and IEEE Big Spatial Data (BSD) workshop 2021.
Program Committee (Tentative)
● Mahdi Boukhechba, University of Virginia
● Ahmed Eldawy, University of California Riverside, email@example.com
● Yan Huang, University of North Texas
● Mohamed Khalifa, Alexandria University
● Xiao Liu, University of Arkansas, firstname.lastname@example.org
● Amr Magdy, University of California, Riverside, email@example.com
● Fernando Marianno, IBM T. J. Watson
● Dev Oliver, Esri
● Ayman Taha, Technological University Dublin
● Fusheng Wang, Stony Brook University, firstname.lastname@example.org
● Demetris Zeinalipour, University of Cyprus, email@example.com
● Jianting Zhang, City College of New York and CUNY Graduate Center, firstname.lastname@example.org
● Amin Pahlavani, email@example.com