It is all good to have access to large volume of data generated with high velocity which normally spans variety of domains and usually comes with levels of veracity. However, the importance of data becomes visible only when we turn it into a value. In particular, in the domain of “Big Spatial Data”, we deal 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 spatio-temporal data in a timely manner.

Call for Papers

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”. 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: Management
  • ● Big Spatial Data: Mining and Analysis
  • ● Big Spatial Data: API services
  • ● Big Spatial Data: Stream Processing
  • ● Big Spatial Data: Privacy and Authentication
  • ● Big Spatial Data: Prediction Models
  • ● Big Spatial Data: Geo-sensing
  • ● Big Geospatial Information Retrieval and Crowdsourcing
  • ● Big Geosocial Networks
  • ● 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: Fundamentals and Theory
  • ● Big Spatial Data: NoSQL and NewSQL Data Stores
  • ● Big Spatial Data: Applications

Important Dates

Paper Submission Deadline: Oct 10, 2018 Oct 17, 2018
Notification of Acceptance: Nov 1, 2018
Camera-Ready Submissions: Nov 15, 2018
Workshop Date: December 13, 2018


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 categories 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

Keynote: Big Spatial Data From Two Perspectives

Abstract: Two different perspectives! Perspective 1: Spatial data is a major percentage of the Big Data wave. It is the time for all Big Data analysis techniques to take role in deriving value from the Big Data that has spatial attributes. Perspective 2: Spatial techniques have been amazing in the analysis of spatial and multidimensional data. It is the time for the spatial folks to use their techniques to invade the Big Data world and use their techniques to analyze and derive value from all sorts of Big Data, whether the data is spatial or not. Regardless of which side you are on, let’s put the two perspectives in the same room and discuss it within the Big Spatial Data fans.

Bio: Mohamed Ali is an associate professor at the School of Engineering and Technology, University of Washington, Tacoma. Mohamed’s research interests include the processing, analysis and visualization of data streams with geographic and spatial information. For the past decade, Mohamed has been building commercial spatiotemporal data streaming systems to cope with the emerging Big Data requirements.
In 2006, Mohamed joined the SQL Server group at Microsoft and, 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. While at Microsoft, Mohamed has been also an affiliate of the University Washington where he taught database, data streaming and GIS classes. In 2014, Mohamed has joined the School of Engineering and Technology, University of Washington, Tacoma where he serves as the director of the Center for Data Science and as the Graduate Program Coordinator.


Start End Event
7:20 8:15 Registration
8:15 9:45 Paper Presentation Session: Data Management
8:15 8:45 Leveraging Spatio-Temporal Soccer Data to Define a Graphical Query Language for Game Recordings
  • ● Keven Richly
8:45 9:15 Concept and Analysis of Information Spaces to improve Prediction-Based Compression
  • ● Ugur Cayoglu, Frank Tristram, Jörg Meyer, Tobias Kerzenmacher, Peter Braesicke, and Achim Streit
9:15 9:45 Accelerating Cross-Matching Operation of Geospatial Datasets using a CPU/GPU Hybrid Platform
  • ● Chao Gao, Furqan Baig, Hoang Vo, Yangyang Zhu, and Fusheng Wang
9:45 10:05 Coffee Break
10:05 11:05 Paper Presentation Session: Pattern Discovery
10:05 10:35 Deriving Real-time City Crowd Flows by Heterogeneous Big Urban Data
  • ● Bo Tang, Chuan Yang, Long Xiang, and Jian Zeng
10:35 11:05 Impact of Trajectory Segmentation on Discovering Trajectory Sequential Patterns
  • ● Somayah Karsoum
11:05 12:05 Paper Presentation Session: Mobile
11:05 11:35 A Data-driven Impact Evaluation of Hurricane Harvey from Mobile Phone Data
  • ● Aude Marzuoli and Fengmei Liu per Presentation Session: Learning and Data Mining
11:35 12:05 RiSC: Quantifying Change After Natural Disasters To Estimate Infrastructure Damage With Mobile Phone Data
  • ● Xavier Andrade, Fabricio Layedra, Carmen Vaca, and Eduardo Cruz
12:10 13:20 Lunch
13:20 14:05 Paper Presentation Session: Learning and Data Mining
13:20 13:50 On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network
  • ● Tobias Skovgaard Jepsen, Christian Søndergaard Jensen, Thomas Dyhre Nielsen, and Kristian Torp
13:50 14:05 Semantic Segmentation of Complex Road Environments from Aerial Images Using Convolutional Neural Networks (Short Paper)
  • ● David Schweitzer and Rajeev Agrawal
14:05 15:05 Keynote: Mohamed Ali, Big Spatial Data From Two Perspectives
15:05 15:25 Coffee Break
15:25 17:10 Paper Presentation Session: Data Analysis
15:25 15:55 Multi-Class Object Detection from Aerial Images Using Mask R-CNN
  • ● David Schweitzer and Rajeev Agrawal
15:55 16:25 Spatio-Temporal Multiple Geo-Location Identification on Twitter
  • ● Kambiz Ghoorchian and Sarunas Girdzijauskas
16:25 16:40 Trajectory Cluster Lifecycle Analysis: An Evolutionary Perspective (Short Paper)
  • ● Ivens Portugal, Paulo Alencar, and Donald Cowan
16:40 17:10 A Practical Expert System with (Near) Real-Time Analysis of Large Spatial Sets of Air Traffic Data
  • ● Vasily Sidorov, Wee Keong Ng, and Mohamed Faisal Bin Mohamed Salleh
17:10 17:20 Adjourn


See 2018 IEEE Big Data Conference Registration Page.


See 2018 IEEE Big Data Conference Accommodations Page.


Workshop Co-chairs:

Farnoush Banaei-Kashani, , University of Colorado Denver
Siyuan Lu, , IBM T.J. Watson Research Center
Chengyang Zhang, , Amazon
Abdeltawab Hendawi,, University of Virginia

Program Committee:

● Yan Huang, University of North Texas
● Fusheng Wang, Stony Brook University
● Jianting Zhang, City College of New York and CUNY Graduate Center
● Ahmed Eldawy, University of California Riverside
● Dave Oliver, ESRI
● Amr Magdy, University of California, Riverside
● Mohamed Khalifa, Alexandria University
● Xiao Liu, University of Arkansas


● Shahab Helmi,