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, it is all useful 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 and location-aware devices. 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.
Keynote: Big Spatial Data @ Facebook
Big Geospatial Data at scale has all the challenges of data at scale along with some quirks very specific to spatio-temporal data. However, these very quirks (like the bounds of latitude/longitude, Euclidean vs. great circle distances, the "true" shape of the earth and the extremely skewed distribution of geospatial features) can be leveraged into interesting and productive trade-offs to offset and address these challenges. With more and more mobile devices thrown into the mix (both as producers and consumers of spatio-temporal data), realtime and accurate lookup of points and polygons based on GPS locations and queries about k-nearest and Top-K based on geospatial contexts are a very common and relevant problem. At the same time, providing scalable offline aggregation and query capabilities of spatio-temporal data for analytics use cases becomes vital to making sense of it.
The Facebook Location Infrastructure team handles spatio-temporal data at Facebook scale (using a mix of in-house and open source technologies and pragmatic trade-offs/decisions). This presentation will cover various design decisions and architectural choices taken to ramp up Trillions of operations per day on a heterogeneous mix of spatio-temporal data (for both online and analytics oriented use cases).
● Xiaoming Gao, Research Scientist at Facebook Inc.
Xiaoming graduated from Indiana University with a PhD in Jan 2015 and currently works at Facebook as a Research Scientist in the Maps team. His thesis work and previous research were related to topics in Distributed Systems, Cloud Computing, Big Data processing, and Social Media Data analysis. His current project is about applying deep learning techniques for object recognition in large scale satellite imagery processing. Google scholar profile
● Saurav Mohapatra, Software Engineer at Facebook Inc.
Saurav works for Facebook on the Location Infrastructure team building Geospatial Indexing and Querying services that handle Spatio-temporal Data at Facebook Scale. Previously he worked for Salesforce.com as an Architect for their real-time infrastructure and prior to that was the co-founder/Director of Technology for the web conferencing startup Dimdim (acquired by Salesforce.com in 2011).
● Lihan Bin, Software Engineer at Facebook Inc.
Lihan works for Facebook on the Location Infrastructure team. Prior to Facebook, he worked on GPGPU standard OpenCL at Qualcomm and served as Qualcomm representative in Khronos Group and HSA foundation. Prior to that he worked for AMD on GPGPU performance and tooling.
Call for Papers
The aim of this workshop, which is held in conjunction with the IEEE International Conference on Big Data, is to bring together researchers and experts from academia, government and industry who are actively addressing problems in the area of Big Spatial Data management and analysis. This workshop gives the audience the opportunity to discuss the lessons 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: 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
Extended Paper Submission Deadline: Oct 12, 2016
Notification of Acceptance: Nov 1, 2016
Camera-Ready Submissions: Nov 15, 2016
Workshop Date: Dec 5-8, 2016
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
|8:10||10:00||Paper Presentation Session: Algorithms and Data Quality|
Big Data Computation of Taxi Movement in New York City
A Comparative Study of Dual-tree Algorithm for Computing 2-Body Statistics in Spatial Data
The SMART Approach to Comprehensive Quality Assessment of Site-Based Spatial-Temporal Data
Towards a Provenance-Aware Spatial-Temporal Architectural Framework for Massive Data Integration and Analysis (Short Paper)
|10:30||12:10||Paper Presentation Session: Platforms and Applications|
Big Data Development Platform For Engineering Applications (Short Paper)
A Survey of the Big Spatial Data Technology Landscape
IBM PAIRS Curated Big Data Service for Accelerated Geospatial Data Analytics and Discovery (Short Paper)
Linked Data View Methodology and Application to BIM Alignment and Interoperability
|2:00||3:30||Paper Presentation Session: Clustering|
Using Parallel Hierarchal Clustering to Address Spatial Big Data Challenges
Adapting K-Means Clustering to identify Spatial Patterns in Storms
Symmetric Repositioning of Bisecting K-means Centers for Increased Reduction of Distance Calculations for Big Data Clustering
Keynote: Big Spatial Data at Facebook
|4:45||6:15||Paper Presentation Session: Imagery Analysis|
Determining Feature Extractors for Unsupervised Learning on Satellite Images
Large-Scale Solar Panel Mapping from Aerial Images Using Deep Convolutional Networks
⚫ Farnoush Banaei-Kashani, firstname.lastname@example.org , University of Colorado Denver
⚫ Chengyang Zhang, email@example.com , Amazon
⚫ Abdeltawab Hendawi, firstname.lastname@example.org, University of Virginia
⚫ 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
⚫ Dev Oliver, ESRI
⚫ Chi-Yin Chow, City University of Hong Kong, Hong Kong
⚫ Youying Shi, email@example.com
⚫ Shahab Helmi, firstname.lastname@example.org