Big Data Analytics for Smart Cities IAMOT 2015, June Cape Town- South Africa Siemens AG All rights reserved. siemens.

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1 Dr. Osman Ahmed, Ph.d. P.E. Senior Director and Head of Innovation Building Technologies. Big Data Analytics for Smart Cities IAMOT 2015, June Cape Town- South Africa

2 Two stories: Big Data Analytics Challenges and Opportunities: How Target figured out a Teen girl was pregnant before her father did- Forbes Big data analytics for Target contributed in ravishing growth from $44b in 2002 to $67b in 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 counties Reported by NPR.

3 Big Data:

4 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

5 Today s Smart City Solutions:

6 Today s Smart City Solutions:

7 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 Data volume 40 zettabytes by 2020 Worldwide revenue 4.1trillion in 2014 Revenue from apps alone amounted to US$72 billion in 2013 and will more than double by 2017 Worldwide revenue of the IT and communications industries reached a record 4.1 trillion in Revenue from apps US$72 billion in % rise 10% increase leads to in per capita GDP Digitalization boosts GDP a 10% increase in the digitalization level of a country leads to a 0.75% rise in per capita GDP. Page 7 February 2015

8 Digitalization at Siemens Leveraging technologies to deliver customer value Leveraging key technology enablers along our entire portfolio to create next level of customer benefits. Big Data & Analytics Digitalization Design & Engineering Highest productivity & shortened time-to-market Maintenance & Service Predictive, prescriptive & efficient services Cloud Mobile & Collaboration Connectivity & Internet of Things Automation Electrification Operations Next level of flexibility & resilience in operations Security, ease-of-use, manageable complexity Page 8 February 2015

9 Data Analytics opportunity for future cities Siemens Picture of Future

10 Data Analytics for hopeful cities Future Hope 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 Misery Poverty

11 Today s City Solutions: 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 Optimize operation Performance analytics Efficiency based on observation Apply data to Rules Page 11 July 2013 Leverage Siemens For internal use only. All rights reserved

12 Tomorrow s City Solutions: Tomorrow: Converged data using analytics create valuable knowledge and insights Business Citizens City City Data Plant Utility 3 rd party Site Enterprise Meter Uncertain Variety Big Volume Speed Data Analytics Sample Discover Modify Mine Model Learn Simulate Predictive Analytics Data create rules Cognitive rules Potential Performance Behavior optimization Business intelligence Identify patterns Hidden intelligence Discovery from learning Page 12 July 2013 Leverage Siemens For internal use only. All rights reserved

13 Data Analytics Architecture: Siemens Data AG Source All rights reserved /article_deploy/html/images/sensors-14- siemens.com 09582f png

14 Data Analytics Architecture- Data Source: Merging and Synergistic Data Sources IOT Siemens AG All rights reserved /article_deploy/html/images/sensors- siemens.com f png

15 IOT for Smart City: Pervasive Ubiquitous Low-cost Edge-computing Connectivity

16 Data Analytics Architecture- Data Source: Data Management and Integration Data Access Layer (OBDC/JDBC ) Data Storage Layer (SQL, OLAP.) Data Integration Layer (Parsing )

17 Data Analytics Architecture- Data Source: Statistics Genetic Algorithm Text Mining OR Neural Network Natural Language Processing Reasoning Diagnostics Signal Processing Cognitive Rules Fuzzy Logic Computer Vision Video Analytics

18 Data Analytics for future cities

19 Conceptual Smart and Sustainable City:

20 Conceptual Smart City: Data Analytics for future cities

21 An Overview of City Smart Service and Inspection Good Performers Continue monitoring Cluster and profile Trend patterns Keep learning IOT and Multiple Systems Raw Data Stream Support heterogeneous protocols Data Management Platform Data Stream Data Analytics and Machine Learning Platform Classifier Clusters Neural Network Genetic Algorithm Data Management Platform Results/Output Decision and Deployment Business Service Results/Output Poor Performers Apply FDD Apply Predictive Analytics Page 21 Wednesday, September 23, 2015 Restricted Siemens AG 2013 All rights reserved.

22 Big Data Analytics for Smart Cities: Case Study: City of Chicago. Dept. of Public Health. Restaurant Inspection 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 Solution: Tobacco & alcohol license Age of Restaurant Time since last inspection Location Garbage & sanitation complaints Nearby burglaries Three day avg. high temperature Machine Learning Platform Predict Violation of License Results: Predicted earlier More violators Better utilization

23 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 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 NPR Radio: Shankar Vedantam: February, 2015

24 Big Data Analytics for Smart City: Louisville, Kentucky: Has one of the nation s worst Asthma problem A City cloud tracks usage of inhaler realtime 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

25 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.