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: Geosensing
  • ⚫ 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 6, 2017 Oct 13, 2017
Notification of Acceptance: Nov 1, 2017 Nov 8, 2017
Camera-Ready Submissions: Nov 15, 2017
Workshop Date: Dec 11-14, 2017


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 submissoins 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: 8 to10 pages
Short/Demo Papers: 4 pages
Position/Visioin Papers: 4 pages;

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 submissoin website

Keynote 1

Abstract: The world of digital data discovery has been revolutionized by the ability to index and rapidly search information on the web, on social networks, and in business transactions (E.g. 45 billion web pages have been indexed allowing to search and discover them in ~ 0.5 second). Despite all these advances, insights from geospatiotemporal data -- information collected across space and time, such as weather data and models, satellite and aerial imagery, map information and data from devices and sensors from the Internet of Things (IoT) -- remain relatively “dark” and are difficult search and discover due to its complexity, formats but most importantly to its enormous size. By some estimations, geospatiotemporal data is larger than any other data including social data growing by many hundreds of Exabytes per month. In this presentation we discuss new approaches for scalable geospatiotemporal data processing and analytics. The talk will include a series of examples illustrating complex analytics on multi-modal data sets including applications to weather, agriculture, and renewable energy and land use recognition.

Bio: Dr. Hendrik F. Hamann is currently a Senior Manager and Distinguished Research Staff Member at the IBM T.J. Watson Research Center, Yorktown Heights, NY. He received his PhD from the University of Göttingen in Germany. In 1999 he joined the IBM T.J. Watson Research Center, where is leading the Physical Analytics and cognitive Internet of Things program. Hamann’s current research interest includes sensor networks, sensor-based physical modeling, machine-learning, artificial intelligence as well as big data technologies. Hamann has authored and co-authored more than 100 peer-reviewed scientific papers and holds over 110 patents and has over 100 pending patent applications. Dr. Hamann is an IBM Master Inventor, a member to the IBM Academy of Technology and has served on governmental committees such as the National Academy of Sciences, the National Science Foundation and as an industrial advisor to Universities. He won several awards including the 2016 AIP Prize for Industrial Applications of Physics. He is a member of the American Physical Society (APS), Optical Society of America (OSA), The Institute of Electrical and Electronics Engineers (IEEE) and the NY Academy of Sciences.

Keynote 2

Abstract: Much in our daily lives is limited by time. There are 86,400 seconds in a day and we are in a constant battle to make the most of them. More time spent on research may mean missing out on time with our families or losing sleep. Those of us in the “Big Spatial Data” realm are far from immune to this struggle. The greater the volume of data the more time it takes to process that data and with the truly massive amounts of information that are available today, that loss of time may become untenable. One of the most efficient solutions to this problem is to use a distributed computing framework to parallelize the work. However, most of these frameworks utilize a single dimensional key-value store which is a problem when you need to preserve locality in multiple dimensions. This is where the GeoWave open source framework comes in ( At its core, GeoWave is a software library that connects the scalability of various distributed computing frameworks and key-value stores with modern geospatial software to store, retrieve, and analyze massive geospatial datasets. The multidimensional indexing algorithms in GeoWave preserve locality in any number of dimensions which decreases your processing time by multiple orders of magnitude. Jobs that previously took hours can be performed in seconds which makes it possible to fully utilize these massive datasets and makes that battle against the clock much easier for analysts and researchers.

Bio: Michael Whitby is a Software Engineer in the Radiant Solutions group of Maxar Technologies (formally Digital Globe). He works on the GeoWave open source project and has had his writing about the framework published in Advances in Spatial and Temporal Databases.
He is a U.S. Navy veteran and received his masters in Software Engineering from Pennsylvania State University.


Start End Event
7:20 8:00 Registration
8:00 10:00 Paper Presentation Session: Data Management and Data Generation
8:00 8:30 All in One: Encoding Spatio-Temporal Big Data in XML, JSON, and RDF without Information Loss
  • ● Peter Baumann, Eric Hirschorn, Joan Maso, Vlad Merticariu, and Dimitar Misev
8:30 9:00 Spaten: a Spatio-temporal and Textual Big Data Generator
  • ● Thaleia Dimitra Doudali, Ioannis Konstantinou, and Nectarios Koziris
9:00 9:30 SQL versus NoSQL Databases for Geospatial Applications
  • ● Elena Baralis, Andrea Dalla Valle, Paolo Garza, Claudio Rossi, and Francesco Scullino
9:30 9:45 Scalable Parallel Data Loading in SciDB (Short Paper)
  • ● Sangchul Kim, Junhee Lee, Taehoon Kim, and Bongki Moon
9:45 10:00 Towards development of spark based agricultural information system including Geo-spatial data (Short Paper)
  • ● Purnima Shah, Deepak Hiremath, and Sanjay Chaudhary
10:00 10:20 Coffee Break
10:20 11:20 Keynote #1: Dr. Hendrik F. Hamann, IBM T.J. Watson Research Center, Yorktown Heights, NY
11:20 12:20 Paper Presentation Session: Pattern Discovery
11:20 11:50 Discovering Dynamic Patterns of Urban Space via Semi-Nonnegative Matrix Factorization
  • ● Zhicheng Liu, Jun Cao, Sannyuya Liu Junyan Yang, and Qiao Wang
11:50 12:05 Multiscale Graph Theoretical Tools Reveal Subtle Patterns in Big Geospatial Data (Short Paper)
  • ● Ronald Hagan, Charles Phillips, Michael Langston, and Bradley Rhodes
12:05 12:20 Challenges and Trends about Smart Big Geospatial Data: A Position Paper (Short Paper)
  • ● Victor Saquicela, Luis Vilches, and Andrés Tello
12:20 13:30 Lunch
13:30 15:30 Paper Presentation Session: Learning and Data Mining
13:30 14:00 Identifying Coherent Anomalies in Multi-Scale Spatio-Temporal Data using Markov Random Fields
  • ● Adway Mitra
14:00 14:30 A Map-Based Visual Analysis Method for Patterns Discovery of Mobile Learning in Education with Big Data
  • ● Dongbo ZhouHao Li, Sannyuya Liu, Bo Song, and Xiaohua Hu
14:30 15:00 Techniques for Efficient Detection of Rapid Weather Changes and Analysis of their Impacts on a Highway Network
  • ● Adil Alim, Aparna Joshi, Feng Chen, Catherine T. Lawson
15:00 15:30 Road Map Extraction from Satellite Imagery Using Connected Component Analysis and Landscape Metrics
  • ● Kulsawasd Jitkajornwanich, Peerapon Vateekul, Teerapong Panboonyuen, Siam Lawawirojwong, and Siwapon Srisonphan
15:30 15:50 Coffee Break
15:50 16:50 Keynote #2: Michael Whitby, Digital Globe
16:50 18:20 Paper Presentation Session: Data Visualization and Analysis
16:50 17:20 Optimal Viewpoint Finding for 3D Visualization of Spatio-Temporal Vehicle Trajectories on Caution Crossroads Detected from Vehicle Recorder Big Data
  • ● Masahiko Itoh, Daisaku Yokoyama, Masashi Toyoda, and Masaru Kitsuregawa
17:20 17:50 Spatiotemporal Visualization of Traffic Paths Using Color Space Time Curve
  • ● Savitha Baskaran, Shiaofen Fang, and Shenghui Jiang
17:50 18:20 A Tale of Two Cities: Analyzing Road Accidents with Big Spatial Data
  • ● Rene Richard, and Suprio Ray
18:20 18:30 Adjourn


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:

Chi-Yin Chow, City University of Hong Kong
⚫ Ahmed Eldawy, University of California Riverside
⚫ Xiaoming Gao, Facebook
⚫ Yan Huang, University of North Texas
⚫ Xiao Liu, University of Arkansas
⚫ Fernando Marianno, IBM T. J. Watson Research Center
⚫ Dev Oliver, ESRI
⚫ Xiaoyan Shao, IBM T.J. Watson Research Center
⚫ Fusheng Wang, Stony Brook University
⚫ Alan Woodley, Queensland University of Technology
⚫ Jianting Zhang, City College of New York and CUNY Graduate Center
⚫ Xun Zhou, The University of Iowa


⚫ Shahab Helmi,