1. Location Powers: Big Data Webinar
22 November 2016

2. Agenda for Today’s Webinar
Introduction to Location Powers and Webinar
Denise McKenzie, OGC
Summary of Sept 20th Workshop
George Percivall, OGC
Management and Dissemination of Earth Observation Data in a Big Data World
Jeff Walter, NASA
Exploring Strategies For Optimizing Knowledge Derivation From Imagery
Dan Getman, DigitalGlobe
Summary and next steps on Big Data

3. Summary of Sept 20th Workshop Location Powers: Big Data
Copyright © 2016 Open Geospatial Consortium

4. Location Powers:Big Data, 20 Sep 2016

5. Use Cases for Big Geo Data
High Velocity Ingest
Geospatial Databases
Entity-oriented Spatial-temporal analytics
Grid-oriented Spatial-temporal analytics
Feature
Fusion
GeoAnalytics, Machine Learning
Remote sensed data processing
Machine Learning
Spatial Modeling
IoT Message Streaming
Built environment models
Array
databases
Users and
consuming
apps
Social Media Message Processing
Observation
Sources
NoSQL
databases
Integrated environmental models
ETL Stream processing using RDF
Graph
databases
Modeling and simulation
Wide Area Motion Imagery
SQL
databases

6. Sessions 1 Opening; 2 Obtaining Big Data

7. Population Distribution and Dynamics Modeling
LandScan Global Ambient Population Distribution (~ 1km)
Increase in spatial and temporal resolution
Top Down and Bottom Up Approach
Ambient and Day/Night Population Distribution (~ 90 m)
LandScan HD
Rapid Settlement Identification and Characterization
Facility Level Population Density
Population Density Tables
Settlement Mapping
Slide: Jibo Sanyal, ORNL

8. Inputs to Geospatial Big Data
NIST Public Big Data Working Group with 5 working groups: Requirements and Use Cases, Definitions and Taxonomies, Reference Architecture, Security and Privacy and Technology Roadmap
30% of uses cases were geospatial
80% of use cases were streaming
Follow up activities extending work and building exemplar use cases defined with DevOps so can be used on multiple infrastructures: HPC, Docker, OpenStack, AWS
Two Streaming workshops at
Many important streaming geospatial use cases
NSF SPIDAL (Scalable Parallel Interoperable Data Analytics Library) project developing HPC-ABDS High Performance Computing enhanced Apache Big Data Stack
Slide Source: Geoffrey Fox, Indiana Univ.

9. Session 3. Maintaining Big Data

10. Big Data: Driving Forces
09/20/2016
Copyright 2016, JCC Consulting, Inc.
Big Data: Driving Forces
Inexpensive storage of large volumes of data
Inexpensive compute power
Next Generation Analytics
Moving from off-line to in-line embedded analytics
Explaining what happened
Predicting what will happen
Operating on
Data at rest – stored someplace
Data in motion – streaming
Multiple disparate data sources
Look at available data and wonder what answers are hidden there
Slide Source: Keith W. Hare, JCC Consulting, Inc.

11. Copyright 2016, JCC Consulting, Inc.
09/20/2016
Copyright 2016, JCC Consulting, Inc.
“Big Data” Data Types
Traditional Data Types
Character
Numerical
Date/Time/Timestamp
Large Objects – LOB/BLOB/CLOB
“Big Data” Data Types
Multi-dimensional arrays
Images/video
Documents 
Loosely formatted data
Objects
Spatial
Slide Source: Keith W. Hare, JCC Consulting, Inc.

12. Apache Projects Geospatially Enabled*
*not exhaustive
Slide Source: Rob Emanuele, Azavea #LPBigData

13. Slide Source: Rob Emanuele, Azavea #LPBigData

14. The Land Change Monitoring Assessment and Projection (LCMAP) information system
Slide Source: Glenn Guempel, USGS #LPBigData

15. Where We Want To Be Download as Last Resort Mentality
The Land Change Monitoring Assessment and Projection (LCMAP) information system
Where We Want To Be
Download as Last Resort Mentality
Store data in unzipped, optimal formats ready for direct processing by standard services or custom processes.
Provide basic visualization, analysis and extraction functions through services on an open platform.
The platform additionally provides the potential processing capacity for building unforeseen custom workflows and processes against big data.
Analysis Ready Data
We believe over years the download mentality will diminish.
Storing data in a ready-to-be-used format will allow users to access data without downloading.
Service Functions will be available for basic visualization, analysis and extraction of data.
Only download what is needed - perhaps the results rather than all the raw data.
Virtual Platforms, like current commercial clouds, will mature and provide cost-effective, on-demand capacity to process big data.
Custom processes and workflows can be supported by allowing users to spin up large infrastructure components, process the data, and shutdown without ongoing costs.
Slide Source: Glenn Guempel, USGS #LPBigData

16. Session 4. Analyzing Big Data

17. Earth Server: Datacubes At Your Fingertips
Intercontinental initiative: EU + US + AUS
started 2011
Agile Analytics on 3D, 4D Earth & Planetary datacubes
Rigorously standards: OGC WMS + WCS + WCPS
EU rasdaman + US NASA WorldWind
100s of TB sites now, next: 1+ PB
Uni
Jacobs
PML
NCI Australia
ESA
MEEO
MWF EC

18. Science & GIS Tool Interfacing
General-purpose scientist tools:
Java, C++
python, R (under work)
Geo tools:
MapServer, GDAL, QGIS, OpenLayers, Leaflet, NASA WorldWind, ...
OGC WCS Core & INSPIRE WCS Reference Implementation
Can interface to all tools supporting OGC‘s „Big Geo Data“ standards suite

19. Moving Features
Location data (ID of object, latitude, longitude, time) is one of the typical bigdata: Moving Features
Most people have cell-phone (smart phone)
Vehicle navigation system measures car locations
Vessels and aircrafts locations are managed due to security
And so on.
The demand for Moving Features is very rapidly increasing
Many Applications
Moving Features
Mobile
Objects
Disaster management
Road traffic control

20. Applications of Moving Features bigdata
Many kinds of Moving Features are used…,
“Data integration” is a key point to produce more value
Disaster management
Traffic management
Integration of tsunami and evacuation simulation
Aircrafts, vessels, vehicles, and pedestrians traffic
Indoor pedestrian flow
Sports
Tracks of visitors to shopping-malls
are useful for marketing
Tracks of soccer players and a ball
are useful for considering tactics

21. L O C A T E
Geo-enrichment Allows a wide variety of datasets to be appended to a data record using a common spatial ID. - What are the property attributes of this insured property? - What demographic group does this customer belong to? - What businesses are in this area of poor network coverage? Analytics Reduce the complexity of billions of transactional records by assigning data to geographic bins and aggregating results Is average 4G network coverage in this area better than a competitor? - Is the accumulated exposure at risk of hurricane damage too high? - Is this data point inside or outside of a geofence?

22. Session 5. Big Data Applications Panel

23. Emergent Themes from Workshop
Loosely-coupled PB archives based on open standards for rapid geospatial information product creation at any scale
Analysis Ready Data
We live in a download mentality. How do we move to answering questions
Focus shifting from understanding what happened last week to being able to predict what will happen next week
Take better advantage of developments in Big Data Proper, which is only tangentially interested in Big Geo Data
Multiple Applications:
Telecommunications, property casualty insurance, financial services, Energy Monitoring and prediction, Population Dynamics, Settlement Mapping
Input to Testbed 13, e.g., SpaceNet

24. Agenda for Today’s Webinar
Introduction to Location Powers and Webinar
Denise McKenzie, OGC
Summary of Sept 20th Workshop
George Percivall, OGC
Management and Dissemination of Earth Observation Data in a Big Data World
Jeff Walter, NASA
Exploring Strategies For Optimizing Knowledge Derivation From Imagery
Dan Getman, DigitalGlobe
Summary and next steps on Big Data

25. © 2016 Open Geospatial Consortium
OGC Actions on Big Data
Location Powers: Big Linked Data Workshop
Delft, 22 March 2017
Publish “Big Geo Data - White Paper”
Apply/extend OGC Standards to Big Data
WMS/WMTS, WFS, WCS/WCPS, WPS
Moving Features Encoding
Discrete Global Grid Systems (DGGS)
Conduct OGC Innovation Program Testbeds
Engineering Reports into Best Practices
OGC Testbed 13
Coordinate: JTC 1 Big Data, Apache, Location Tech
OGC Big Data Domain Working Group
© 2016 Open Geospatial Consortium 25

26. OGC Big Data Domain Working Group
Public forum for geospatial Big Data interoperability, access, and especially analytics.
Encourage collaborative development among participants representing many organizations and communities,
Ensure appropriate liaisons to other Big Data relevant working groups, both inside and outside OGC.
list - Open to public:
© 2016 Open Geospatial Consortium 26

27. Agenda for Today’s Webinar
Introduction to Location Powers and Webinar
Denise McKenzie, OGC
Summary of Sept 20th Workshop
George Percivall, OGC
Management and Dissemination of Earth Observation Data in a Big Data World
Jeff Walter, NASA
Exploring Strategies For Optimizing Knowledge Derivation From Imagery
Dan Getman, DigitalGlobe
Summary and next steps on Big Data