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.
Important Dates
Paper Submission Deadline: October 1, 2022, October 8, 2022 (Extended Deadline)
Notification of Acceptance: November 1, 2022, November 14, 2022, November 16, 2022
Camera-Ready Submissions: November 20, 2022, November 24, 2022, November 27, 2022
Video Presentation Submission: November 28, 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.
Papers have to follow the IEEE official template. Maximum paper length allowed is:
● Full Papers: 10 pages, including all figures, tables, and references
● Poster Papers: 3 pages, including all figures, tables, and references
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.
Camera Ready Submission Instructions:
IEEE BigData 2022 Workshops Camera Ready Instruction
Camera Ready Submission Link:
Camera Ready Paper / Copyright Form Submission / Registration Receipt
Video Upload Instructions:
BIG DATA 2022 - Video upload instructions
Video Presentation Submission Link:
Conference Publishing Services
Keynote: Accelerating Discovery and Conservation of the Nasca Geoglyphs using Artificial Intelligence
Abstract We will present the research results on the Nasca geoglyphs, a World Heritage Site, over the past 15 years and discuss how artificial intelligence (AI) technology resolves problems associated with traditional archaeological research methods. We have been conducting extensive field surveys of Nasca Pampa where a large number of geoglyphs are expected to be discovered utilizing satellite imagery, aerial photography and drone imagery to understand the distribution of the geoglyphs and clarify the period and the purpose. It has been about 100 years since the first discovery, however the number of geoglyphs that exist in the entire pampa has not been elucidated yet because the area of the pampa is vast, approximately 20 km by 15 km. Our research using AI technology can really enhance the coverage of the pampa to be investigated and accelerate the discovery process of geoglyphs as well as determine the distribution that is also needed for developing proper geoglyph protection.
Bio: Professor Masato Sakai is an anthropologist affiliated with Yamagata University, Japan. He has conducted research at archaeological sites in Peru (Kuntur Wasi, Chan Chan, Limoncarro, Espiritu Pampa, Pacopampa) since 1989 and elucidated the role played by the arrangement of architecture in temples and royal cities in Andean societies. He has studied the Nasca geoglyphs, a World Heritage Site, through intensive field surveys for 15 years utilizing satellite imagery, LIDAR aerial photographs and drones. Several hundred figurative geoglyphs (animals, humans, etc.) have been discovered by his projects He is leading a research institute which Yamagata University established at Nasca city in 2012 and is engaged in conservation projects of Nasca geoglyphs in collaboration with the Peruvian Ministry of Culture. His current interest is to realize how AI technology can help study of Nasca geoglyphs.
Program
● All times are in JST.
| Start | End | Event |
|---|---|---|
| 8:30 AM | 8:35 AM | Opening Remarks |
| 8:35 AM | 9:35 AM |
Keynote: Accelerating Discovery and Conservation of the Nasca Geoglyphs using Artificial Intelligence
|
| 9:35 AM | 10:05 AM |
FAGR: Fairness-aware Group Recommendation in Event-based Social Networks
|
| 10:05 AM | 10:30 AM | Coffee Break |
| 10:30 AM | 11:00 AM |
Retrieving Top-N Weighted Spatial k-cliques
|
| 11:00 AM | 11:30 AM |
DACMA: Designing space ordering optimizations to scalably manage aerial images
|
| 11:30 AM | 12:00 PM |
Vision Paper: Emergence of an Autonomous Vehicle Secondary Data Market for Breakthrough Applications
|
| 12:00 PM | 12:30 PM |
Trajectory-User Linking Is Easier Than You Think
|
| 12:30 PM | 1:00 PM |
Systematic Analysis of Public Transit Data Availability in Canada
|
| 1:00 PM | 1:30 PM |
Thematic Geo-Density Heatmapping for Walking Tourism Analytics using Semi-Ready GPS Trajectories
|
| 1:30 | 1:45 | Closing Remarks |
Organizers
Workshop Co-chairs
Farnoush Banaei-Kashani, farnoush.banaei-kashani@ucdenver.edu, 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, cyzhang@microsoft.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, hendawi@uri.edu, 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 (2019-2022) and IEEE Big Spatial Data (BSD) workshop (2021-2022).
Program Committee
● Ahmed Eldawy, University of California Riverside, eldawy@ucr.edu
● Mohamed Khalefa, SUNY College Old Westbury
● Xiao Liu, University of Arkansas, xl027@uark.edu
● Amr Magdy, University of California, Riverside, amr@cs.ucr.edu
● Fusheng Wang, Stony Brook University, fusheng.wang@stonybrook.edu
● Demetris Zeinalipour, University of Cyprus, dzeina@cs.ucy.ac.cy
● Jianting Zhang, City College of New York and CUNY Graduate Center, jzhang1@ccny.cuny.edu
● Joon-Seok Kim, ORNL
● Samriddhi Singla, Meta Platforms Inc.
Webmaster
● Amin Pahlavani, amin.pahlavani@ucdenver.edu