Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
®
Big Data for Local Context
George Percivall
OGC Chief Engineer, CTO
Location and Context 2015
3 November 2015
Copyright ...
OGC
®
Power of Location
•  “Location targeting is holy grail for marketers”
– Sir Martin Sorrell, CEO WPP at Mobile World ...
OGC
®
Commercial
39%
Government
27%
NGO
8%
Research
6%
University
20%
The Open Geospatial Consortium
Copyright © 2015 Open...
Contexts &
Possibilities
PRESENT
Behaviors &
Actuals
PAST
Predictions &
Potentials
FUTURE
Source: Jon Spinney, Location In...
5
Marketing
Automaton
Source: Jon Spinney, Location Intelligence, Pitney Bowes
6
By Analyzing Large Volumes of
Historical Mobile Location Data
Married with Place & Demographic
Data, We Can Create Audie...
7
Risk
Analysis
Source: Jon Spinney, Location Intelligence, Pitney Bowes
8
By Analyzing Large Volumes of
Historical Travel Data coupled with
Historical Accident Observations, and
Present Observat...
OGC
®
Geospatial Context via “Space Time Boxes”
Jeff Jonas, IBM Fellow. Chief Scientist, Context Computing Strata + Hadoop...
OGC
®
Spatial Bins used for Local Context
Copyright © 2015 Open Geospatial Consortium
Canberra region with hexagon grid.
OGC
®
Discrete Global Grid Systems
Source: Matt Purss, Geoscience Australia
National
Nested
Grid
SCENZ-Grid
Earth System S...
OGC
®
OGC Discrete Global Grid Systems (DGGS)
• Common criteria and terms for spatial grids
• Facilitate interoperability ...
OGC
®
Big Data Spatial Analytics - GeoWave
Copyright © 2015 Open Geospatial Consortium
Geographic objects and operators in...
OGC
®
Detecting proximity and colocation in time
Copyright © 2013 Open Geospatial Consortium
time
Spatial	
  
plane
1	
  p...
OGC
®
OGC Moving Features Encoding Standard
•  "Moving features" data describes such things as vehicles,
pedestrians, airp...
OGC
®
Transportation Survey	
•  GPS tracks encoded in OGC Moving Features
•  Sharing data by local governments, transit co...
OGC
®
Crime Pattern	
• Predicting regions
more vulnerable to
crime based on
Routine Activity
Theory	
Copyright © 2015 Open...
OGC
®
Layout design	
Pedestrian-tracking to understand movements in facilities,
e.g., shopping malls and transit terminals...
OGC
®
Indoor Geo-Portal
Indoor mCommerce
Emergency
Control
Services for
handicapped persons
Cruise Ship
Hospital
Indoor LB...
OGC
®
Retail Location Analytics
Copyright © 2015 Open Geospatial Consortium
Source: HP Viewpoint paper - Retail location a...
OGC
®
National Retail Federation
In-Store Use Cases
•  Marketing
–  drive traffic to store
–  In-store targeting
•  Custom...
OGC
®
CityGML and IndoorGML
1st layer: Topographic space model
–  building structure
–  geometric-topological model
–  net...
}  The ILA promotes
the deployment of
indoor location
based services in
mobile environment
}  ILA and OGC
working togeth...
OGC
®
Context Discovery across IoT Data Flow
Copyright © 2015 Open Geospatial Consortium
IEEE ACCESS
Data Archive
User
Sma...
OGC
®
OGC SensorThings API for IoT
•  Builds on OGC Sensor Web Enablement (SWE) standards
that are operational around the ...
OGC
®
Region-Centric
Geospatial
Information
Feature-Centric
Geospatial
Information
Human-Centric
Geospatial
Information
De...
OGC
®
Scale of 1 to 1 by Lewis Carroll, 1893
Copyright © 2015 Open Geospatial Consortium
“And then came the grandest idea ...
OGC
®
Join the OGC
Open Geospatial Consortium
www.opengeospatial.org
OGC Standards - freely available
www.opengeospatial.o...
Upcoming SlideShare
Loading in …5
×

Big Data for Local Context

823 views

Published on

Presentation Location and Context World, 2015. Palo Alto, CA November 3-4, 2015.

Abstract: Creating useful local context requires big data platforms and marketplaces. Contextual awareness is relevant to location based marketing, first responders, urban planners and many others. Location-aware mobile devices are revolutionizing how consumers and brands interact in the physical world. Situational awareness is a key element to efficiently handling any emergency response. In all cases, big data processing and high velocity streaming of location based data creates the richest contextual awareness. Data from many sources including IoT devices, sensor webs, surveillance and crowdsourcing are combined with semantically-rich urban and indoor data models. The resulting context information is delivered to and shared by mobile devices in connected and disconnected operations. Standards play a key role in establishing context platforms and marketplaces. Successful approaches will consolidate data from ubiquitous sensing technologies on a common space-time basis to enabled context-aware analysis of environmental and social dynamics.

Published in: Technology
  • Get access to 16,000 woodworking plans, Download 50 FREE Plans... ■■■ http://t.cn/A6hKwZfW
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • ■■■
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Big Data for Local Context

  1. 1. ® Big Data for Local Context George Percivall OGC Chief Engineer, CTO Location and Context 2015 3 November 2015 Copyright © 2015 Open Geospatial Consortium
  2. 2. OGC ® Power of Location •  “Location targeting is holy grail for marketers” – Sir Martin Sorrell, CEO WPP at Mobile World Congress •  By measuring entropy of individual’s trajectory, we find 93% potential predictability in user mobility – Limits of Predictability in Human Mobility, Science 2010 •  1st law of geography: "Everything is related to everything else, but near things are more related than distant things.” –  Waldo Tobler © 2013 Open Geospatial Consortium 2
  3. 3. OGC ® Commercial 39% Government 27% NGO 8% Research 6% University 20% The Open Geospatial Consortium Copyright © 2015 Open Geospatial Consortium Not-for-profit, international voluntary consensus standards organization; leading development of geospatial standards •  Founded in 1994. •  515+ members and growing •  48 standards •  Thousands of implementations •  Broad user community implementation worldwide •  Alliances and collaborative activities with ISO and many other SDO’s Africa 4 Asia Pacific 86 Europe 209 Middle East 34 North America 182 South America 3
  4. 4. Contexts & Possibilities PRESENT Behaviors & Actuals PAST Predictions & Potentials FUTURE Source: Jon Spinney, Location Intelligence, Pitney Bowes
  5. 5. 5 Marketing Automaton Source: Jon Spinney, Location Intelligence, Pitney Bowes
  6. 6. 6 By Analyzing Large Volumes of Historical Mobile Location Data Married with Place & Demographic Data, We Can Create Audience Segments and New Enriched Lifestyle Profiles for Targeting. Source: Jon Spinney, Location Intelligence, Pitney Bowes
  7. 7. 7 Risk Analysis Source: Jon Spinney, Location Intelligence, Pitney Bowes
  8. 8. 8 By Analyzing Large Volumes of Historical Travel Data coupled with Historical Accident Observations, and Present Observations, We Can Assign Estimated Risk & Associated Insurance Premiums to Auto Plans. Source: Jon Spinney, Location Intelligence, Pitney Bowes
  9. 9. OGC ® Geospatial Context via “Space Time Boxes” Jeff Jonas, IBM Fellow. Chief Scientist, Context Computing Strata + Hadoop World 2015
  10. 10. OGC ® Spatial Bins used for Local Context Copyright © 2015 Open Geospatial Consortium Canberra region with hexagon grid.
  11. 11. OGC ® Discrete Global Grid Systems Source: Matt Purss, Geoscience Australia National Nested Grid SCENZ-Grid Earth System Spatial Grid Snyder Grid
  12. 12. OGC ® OGC Discrete Global Grid Systems (DGGS) • Common criteria and terms for spatial grids • Facilitate interoperability and data fusion • Support marketplaces of spatial data • Public comment period to begin next week http://www.opengeospatial.org/projects/groups/dggsswg
  13. 13. OGC ® Big Data Spatial Analytics - GeoWave Copyright © 2015 Open Geospatial Consortium Geographic objects and operators in Apache Accumulo Advanced support for OGC spatial types: 3D and temporal GeoServer plugin for OGC Web Services http://ngageoint.github.io/geowave/
  14. 14. OGC ® Detecting proximity and colocation in time Copyright © 2013 Open Geospatial Consortium time Spatial   plane 1  prism  =  1  leaf  +  1  sweep (&attribute) End  leaf  of  tracks id=1 Id=2 11:11:20.835 11:11:26.215 11:11:28.021 11:11:30.127 (C) (B) (D) (A) OGC Moving Features Standard implements ISO 19141
  15. 15. OGC ® OGC Moving Features Encoding Standard •  "Moving features" data describes such things as vehicles, pedestrians, airplanes and ships. –  This is Big Data – high volume, high velocity. •  CSV and XML encodings of ISO 19141 Copyright © 2015 Open Geospatial Consortium
  16. 16. OGC ® Transportation Survey •  GPS tracks encoded in OGC Moving Features •  Sharing data by local governments, transit companies, etc. •  Reduce traffic congestion using transportation surveys. Copyright © 2015 Open Geospatial Consortium Transportation  survey 1234 3456 2345 4567 1234 3456 12hr  total 24hr  total 123 234 3456 4567 3456 7890 Traffic  DemandsTraffic  Congestions   Smart  phones People  in  the  city   Tracks  measured  by  GPS (encoded  by  Moving  Features)
  17. 17. OGC ® Crime Pattern • Predicting regions more vulnerable to crime based on Routine Activity Theory Copyright © 2015 Open Geospatial Consortium
  18. 18. OGC ® Layout design Pedestrian-tracking to understand movements in facilities, e.g., shopping malls and transit terminals Copyright © 2015 Open Geospatial Consortium
  19. 19. OGC ® Indoor Geo-Portal Indoor mCommerce Emergency Control Services for handicapped persons Cruise Ship Hospital Indoor LBS Indoor Security Indoor Robot Indoor Location Application © 2015 Open Geospatial Consortium
  20. 20. OGC ® Retail Location Analytics Copyright © 2015 Open Geospatial Consortium Source: HP Viewpoint paper - Retail location analytics As an example, Figure 1 shows a department store that has been set up in a grid. Each square in the grid can be labeled with attributes that are significant to the retailer. By tracking the frequency of visits and time spent in a particular department, the interests of nalytics Figure 1. Location grid
  21. 21. OGC ® National Retail Federation In-Store Use Cases •  Marketing –  drive traffic to store –  In-store targeting •  Customer Service –  Locate a product in store –  Shopping List - multi-channel –  Request store associate •  Operations –  Store setup –  Stocking shelves Copyright © 2015 Open Geospatial Consortium Copyright 2013 NRF. All rights reserved.Page 11 Verbatim reproduction and distribution of this document is permitted in any medium, provided this notice is preserved. Interest. This gives the flexibility to report the information in the most relevant format. 2.2Recommended Coordinate System These different systems are in contention, so the recommendation presented in this best practices paper defines one common coordinate system, and one consistent origin system. Given that the ARTS Location Standard’s objective is to locate objects within a store as a whole, the recommended coordinate system has to apply across all ARTS standards. The floor-plan coordinate system is the recommendation as modified herein. The origin is located at the bottom back left position of the object. This is the same origin used within planograms and the ARTS Video Analytics Standard. This presents an interesting solution to a sloping shelf. The position of the origin of the shelf doesn’t change, since it is anchored at the back. If the bottom front left position was used, then when a shelf is sloped it would change the location of the origin, which would complicate the transformation algorithms. The following table highlights how existing system would be affected by adopting this standard: Floor Plan System Planogram System Origin Bottom Back Left of Fixture Bottom Back Left of Fixture Coordinate System Left-Hand Coordinate System Left-Hand Coordinate System X axis Left to Right Left to Right Y axis Front to Back Front to Back Z axis Bottom to Top Bottom to Top Copyright 2013 NRF. All rights reserved.Page 11 Verbatim reproduction and distribution of this document is permitted in any medium, provided this notice is preserved. The origin is located at the bottom back left position of the object. This is the same origin used within planograms and the ARTS Video Analytics Standard. This presents an interesting solution to a sloping shelf. The position of the origin of the shelf doesn’t change, since it is anchored at the back. If the bottom front left position was used, then when a shelf is sloped it would change the location of the origin, which would complicate the transformation algorithms. The following table highlights how existing system would be affected by adopting this standard: Floor Plan System Planogram System Origin Bottom Back Left of Fixture Bottom Back Left of Fixture Coordinate System Left-Hand Coordinate System Left-Hand Coordinate System X axis Left to Right Left to Right Y axis Front to Back Front to Back Z axis Bottom to Top Bottom to Top Graphics source: ARTS Coordinate System & Insertion Points Best Practices Version 1.0 ARTS Coordinate S Points Best Practices Version 1.0 November 29, 2013 – Last Call Working Draft Chairman: Dennis Blankenship V Phuc Do T Members: Graeme Shaw (Team Captain) O Curtis Philbrook J Amit Chetal C Amnon Ribak I Andy Mattice A Arun Hampapur I Bart McGlothin C Marc MacDonald E Doug Wick D In-store coordinate Reference Systems
  22. 22. OGC ® CityGML and IndoorGML 1st layer: Topographic space model –  building structure –  geometric-topological model –  network for route planning 2nd layer: Sensor space model –  Radio/Beacon footprints –  coverage of sensor areas –  transition between sensor areas •  Builds on existing International standards CityGML and IFC © 2015 Open Geospatial Consortium 22
  23. 23. }  The ILA promotes the deployment of indoor location based services in mobile environment }  ILA and OGC working together on standards based architecture
  24. 24. OGC ® Context Discovery across IoT Data Flow Copyright © 2015 Open Geospatial Consortium IEEE ACCESS Data Archive User Smart Things Sensors Smart Wearable Visualisation / Presentation / Recommendation Cloud Intermediary Communication and Processing Devices =Context Discovery Real Time Processing Fig. 6. Data Flow in IoT Solutions in High-level. Context can be discovers in different stages / phases in the data flow. A typical IoT solution may some part of the data flow architecture depending on the their intended functionalities. First we introduce the name of the IoT solution in the column (1) in Table II. We also provide the web page link of the each product / solution. It is important to note that, these products does not have any related academic publication. Therefore, we believe that web page links are the most reliable collects and processes context information such as locatio temperature, light, relative humidity and biometric pressu in order to enhance the visibility and transparency of t supply chain. SenseAware uses both hardware and softwa components in their sensor-based logistic solution. such d Perera, C.; Liu, C.H.; Jayawardena, S.; Min Chen, "A Survey on Internet of Things From Industrial Market Perspective,” Access, IEEE , vol.2, no., pp.1660-1679, 2014 doi: 10.1109/ACCESS.2015.2389854
  25. 25. OGC ® OGC SensorThings API for IoT •  Builds on OGC Sensor Web Enablement (SWE) standards that are operational around the world •  Builds on Web protocols; easy-to-use RESTful style •  OGC candidate standard for open access to IoT devices Copyright © 2015 Open Geospatial Consortium Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Emergence of the Internet of Things Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm!Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm!Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Graphic from Dr. Steve Liang Associate Professor / AITF-Microsoft Industry Chair" OGC SensorThings SWG Chair Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! (Graphic source: Steve Liang, University of Calgary)
  26. 26. OGC ® Region-Centric Geospatial Information Feature-Centric Geospatial Information Human-Centric Geospatial Information Device-Centric Geospatial Information 1980s 1990s 2000s 2010s Source  of  slide:    Steve  Liang,  Univ.  Calgary  and  chairman  of  OGC  Sensor  Web  4  IoT     Progression of geospatial information © 2013 Open Geospatial Consortium, Inc. 26 èTowards Micro-geography
  27. 27. OGC ® Scale of 1 to 1 by Lewis Carroll, 1893 Copyright © 2015 Open Geospatial Consortium “And then came the grandest idea of all! We actually made a map of the country, on the scale of a mile to the mile!” “Have you used it much?” I enquired. “It has never been spread out, yet. The farmers object: they said it would cover the whole country, and shut out the sunlight! So we now use the country itself, as its own map, and I assure you it does nearly as well.”
  28. 28. OGC ® Join the OGC Open Geospatial Consortium www.opengeospatial.org OGC Standards - freely available www.opengeospatial.org/standards OGC on YouTube http://www.youtube.com/user/ogcvideo George Percivall gpercivall@opengeospatial.org Copyright © 2015 Open Geospatial Consortium

×