Dr. Osman Ahmed, Ph.d. P.E. Senior Director and Head of ... · Tomorrow: Converged data using...
Transcript of Dr. Osman Ahmed, Ph.d. P.E. Senior Director and Head of ... · Tomorrow: Converged data using...
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Big Data Analytics for Smart Cities IAMOT 2015, June 08-11
Cape Town- South Africa
Dr. Osman Ahmed, Ph.d. P.E. Senior Director and Head of Innovation
Building Technologies. [email protected]
© Siemens AG 2015. All rights reserved. siemens.com
Two stories: – Big Data Analytics Challenges and Opportunities:
How Target figured out a Teen girl was pregnant before her
father did- Forbes 2012.
Big data analytics for Target contributed in ravishing growth
from $44b in 2002 to $67b in 2012. But how would you handle
privacy and other pitfalls with profiling and other unintended
results?
February, 2015:
Twitter can serve as a dashboard
indicator of a community’s
psychological well being and can
predict rates of heart disease close to
what CDC can do.
Research conducted by Univ. of
Pennsylvania- 1300 counties
• Reported by NPR. www.npr.org
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Big Data:
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Big data analytics for Smart Cities- Background:
Scope:
• City infrastructure such as:
•Buildings, bridges, sewage, water, lighting, streets, transportation, health,
environment, sustainability
• City inspection for public health and safety
Needs:
• Monitoring
• Improve performance
• Provide better service such as security, safety
• Quick response
Constraints:
• Aging infrastructure
• Ever-expanding scope
• Shrinking budget and resources
Goal:
• Big city data analytics can find new solutions and cost-effectively
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Today’s Smart City Solutions:
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Today’s Smart City Solutions:
February 2015 Page 7
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Megatrends
How digitalization is transforming our world
Worldwide data volume doubles every two years.
By 2020, it will have grown to 40 zettabytes – that’s
a 50-fold increase within ten years
Worldwide revenue of the IT and communications
industries reached a record €4.1 trillion in 2014.
Digitalization boosts GDP – a 10% increase in the
digitalization level of a country leads to a 0.75%
rise in per capita GDP.
Revenue from apps alone amounted to US$72 billion
in 2013 and will more than double by 2017
€4.1trillion
Worldwide revenue
in 2014
40 zettabytes
Data volume
by 2020
US$72 billion
Revenue from apps
in 2013
0.75% rise
10% increase leads to
in per capita GDP
February 2015 Page 8
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Digitalization at Siemens
Leveraging technologies to deliver customer value
Digitalization
Automation
Electrification
Design & Engineering Highest productivity &
shortened time-to-market
Maintenance & Service Predictive, prescriptive &
efficient services
Operations Next level of flexibility &
resilience in operations
Security, ease-of-use,
manageable complexity
Big Data &
Analytics
Cloud
Mobile &
Collaboration
Connectivity &
Internet of Things
Leveraging key technology enablers along our entire portfolio to create next level of customer benefits.
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Data Analytics opportunity for future cities
Siemens Picture of Future
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Data Analytics for hopeful cities
Poverty
Misery
Hope
Future
• Dharavi, Mumbai, India.
• One of the largest slums
• Population: 1 million
• Economy: $600’- 1b
• Economy:
• Leather, Garments, Recycle
• Focus on Education
The NY Times: Dec., 2011
For internal use only. All rights reserved Page 11 July 2013 Leverage Service @ Siemens
• Optimize operation
• Performance analytics
• Efficiency based on
observation
• Apply data to Rules
Today: City Performance based on based on observed and operating data
• Sensors
• Meter
• Utility
• 3rd Party
Operating
Data
Structured program
Physics based modeling
Heuristics/ Statiscal
Today’s City Solutions:
For internal use only. All rights reserved Page 12 July 2013 Leverage Service @ Siemens
Tomorrow: Converged data using analytics create valuable knowledge and insights
Meter
City
Business Citizens
Utility
Site
3rd party
Enterprise
Plant
Sample
Modify
Model Learn
Simulate
Discover
Mine
Data Analytics
Big Volume Speed
Uncertain Variety
City Data
• Predictive Analytics
• Data create rules
• Cognitive rules
• Potential Performance
• Behavior optimization
• Business intelligence
• Identify patterns
• Hidden intelligence
• Discovery from learning
Tomorrow’s City Solutions:
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Data Analytics Architecture:
• Data Source
http://www.mdpi.com/sensors/sensors-14-
09582/article_deploy/html/images/sensors-14-
09582f1-1024.png
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Data Analytics Architecture- Data Source:
http://www.mdpi.com/sensors/sensors-14-
09582/article_deploy/html/images/sensors-
14-09582f1-1024.png
Merging and Synergistic Data Sources
IOT
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http://3.bp.blogspot.com/-jHMYBdjkrlA/UBuQQijDyMI/AAAAAAAAATg/Y7pQqn65cx8/s1600/IOTA_MainImage.png
IOT for Smart City:
Pervasive Ubiquitous Low-cost Edge-computing Connectivity
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Data Analytics Architecture- Data Source:
Data Management and Integration
Data Access Layer (OBDC/JDBC……)
Data Storage Layer (SQL, OLAP….)
Data Integration Layer (Parsing…)
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Data Analytics Architecture- Data Source:
Statistics OR Reasoning
Diagnostics
Cognitive
Rules
Genetic
Algorithm
Neural
Network
Fuzzy
Logic
Signal
Processing
Text
Mining
Natural Language
Processing Computer Vision
Video Analytics
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Data Analytics for future cities
http://www.wired.com/2013/07/the-new-horizon-for-bi-and-analytics/
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Conceptual Smart and Sustainable City:
https://connectedtechnbiz.files.wordpress.com/2014/10/smart-city-concept.jpg
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Data Analytics for future cities
Conceptual Smart City:
Wednesday, September 23, 2015 Restricted © Siemens AG 2013 All rights reserved. Page 21
An Overview of City Smart Service and Inspection
Data Management
Platform
IOT and Multiple Systems
Support heterogeneous
protocols
Data Analytics and Machine
Learning Platform
Classifier Clusters
Neural Network Genetic Algorithm
Decision and Deployment
Business Service
Raw Data Stream
Data Management
Platform
Data Stream Results/Output
Results/Output
Good Performers
•Continue monitoring
•Cluster and profile
•Trend patterns
•Keep learning
Poor Performers
•Apply FDD
• Apply Predictive Analytics
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Big Data Analytics for Smart Cities:
http://datasmart.ash.harvard.edu/news/article/delivering-faster-results-with-food-inspection-forecasting-631
Case Study: City of Chicago. Dept. of Public Health. Restaurant Inspection
•15000 Restaurants. 36 City inspectors. Annually,15% restaurants receive violation.
•Public health hazard. Spread of food borne illnesses.
•Predict and prevent early violators
•City teams up with Allstate Insurance Analytics team- PPP project
•Tobacco & alcohol license
•Age of Restaurant
•Time since last inspection
•Location
•Garbage & sanitation complaints
•Nearby burglaries
•Three day avg. high temperature
Solution:
Machine
Learning
Platform
Predict
Violation
of
License
Results:
• Predicted earlier
• More violators
• Better utilization
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Big Data Analytics for Smart City:
Case Study: City of Chicago.
•Publishes neighborhood safety index based on crime rate.
•Neighborhoods suffer because red zones are unsafe to live
Data Analytics:
•Yale University researchers
•Looked into crime rates with the victims profiles in social media
•Meta data
• NPR Radio: Shankar Vedantam: February, 2015
Results:
•Risk of facing a violent crime in red neighborhood really depends on someone’s
network of people he/she knowd and behavior
•Similar to public health crisis such as sharing a needle
•Risk is not random based on where you live but whom you know:
•Primary, secondary, or even tertiary level of connections
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Big Data Analytics for Smart City:
https://www.airlouisville.com/
•A City cloud tracks usage of inhaler real-
time through sensing and mobile app.
•Data points out where usage is high and
under conditions
•Analytics help to improve air quality and
predict Asthma conditions
Louisville, Kentucky: Has one of the nation’s worst Asthma problem
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Big data analytics for Smart Cities- :
Conclusions:
• Big city data analytics has significant opportunities to create new
solutions for both cities and infrastructure
•Creation of values- business, social, economic- should be the goals
•A well planned architecture needs to be implemented for the long-run
•Strategic vision is of paramount importance
•Citizens need to be inspired to participate
•Great collaboration opportunities exist for academicians, industry, and
govt. organizations.