ai for smart city innovations with open data (tutorial)

AI FOR SMART CITY INNOVATIONS WITH OPEN DATA DR. BIPLAV SRIVASTAVA A C M D I S T I N G U I S H E D S C I E N T I S T , A C M D I S T I N G U I S H E D S P E A K E R S E N I O R R E S E A R C H E R A N D M A S T E R I N V E N T O R , I B M R E S E A R C H – I N D I A

1 Tutorial on 27 July 2015 @ IJCAI 2015

Why This Tutorial?

Tutorial on 27 July 2015 @ IJCAI 2015

�  Sustainability is a key imperative of modern societies �  AI techniques have high potential to impact the

world �  But they need data which is not always available �  Open data is often the most promising source to start

making quick impact �  Eventual aim should be to scale innovations with

other data sources

2

What to Expect: Tutorial Objectives


�  The aim of the tutorial is to ¡  Make early and experienced researchers aware, and equip them to create, societal innovations

with AI techniques like semantics, knowledge representation, data integration, machine learning, planning, scheduling, logic, trust and agents, and open data, that is increasingly, readily available, globally from government and other sources.

�  Relation to other tutorials 1.  Tutorial on Composing Web APIs – State of the art and mobile implications, in conjunction with 1st International Conference

on Mobile Software Engineering and Systems (MobiSOFT), held with 39th International Conference on Software Engineering (ICSE), by Biplav Srivastava; Hyderabad, India, June 2, 2014.

2.  Tutorial on AI-Driven Analytics In Traffic Management, in conjunction with International Joint Conference on Artificial Intelligence (IJCAI-13), by Biplav Srivastava, Akshat Kumar, at Beijing, China, Aug 3-5, 2013.

3.  Tutorial describing the traffic space and relevance of AI techniques was held at 26th Conference on Artificial Intelligence (AAAI-12), at Toronto, Canada. Formally called “Tutorial Traffic Management and AI”, by Biplav Srivastava and Anand Ranganathan, its details are at: http://www.aaai.org/Conferences/AAAI/2012/aaai12tutorials.php

4.  Tutorial highlighting planning and scheduling techniques for traffic management was held at ICAPS 2010. Formally called “Planning and Scheduling for Traffic Control” by Scott Sanner. Its details are available at: http://users.cecs.anu.edu.au/~ssanner/Papers/traffic_tutorial.pdf

5.  Tutorial on Open Data in Practice, in conjunction with the World Wide Web (WWW 2012), by Hadley Beeman, in Lyon, France on the 16th of April, 2012. Slides at: http://www.w3.org/2012/Talks/0417-LD-Tutorial/

6.  Tutorial on How to Publish Linked Data on the Web, in conjunction with International Semantic Web Conference (ISWC 2008), by Tom Heath, Michael Hausenblas, Christian Bizer, Richard Cyganiak, Olaf Hartig, Karlsruhe. Slides and video at: http://videolectures.net/iswc08_heath_hpldw/

�  Disclaimer: we are only providing a sample of Smart City space intended to whet audience interest in the available time.

3

Acknowledgements All my collaborators over last 5 years, and especially those in: �  Government agencies around the world

¡  City: Boston, USA; New York/ New Jersey area, USA; Silicon Valley, USA; Dubuque, IA; Dublin, Ireland, Stockholm, Sweden; Ho Chi Minh City, Vietnam; New Delhi, India; Bengaluru, India; Nairobi, Kenya; Tokyo, Japan

¡  Country: India, Singapore

�  Academia ¡  India: IIT Delhi, IISc CiSTUP, IIIT Delhi, IIT BHU ¡  USA: Boston University, Wright State University, University of Southern California,

Arizona State University ¡  Vietnam: Ho Chi Minh University

�  IBM: Akshat Kumar, Anand Ranganathan, Raj Gupta, Ullas Nambiar, Srikanth Tamilselvam, L V Subramaniam, Chai Wah Wu, Anand Paul, Milind Naphade, Jurij Paraszczak, Wei Sun, Laura Wynter, Olivier Verscheure, Eric Bouillet, Francesco Calabrese, Tsuyoshi Ide, Xuan Liu, Arun Hampapur, Nithya Rajamani, Vivek Tyagi, Rauam Krishnapuram, Shivkumar Kalyanraman, Manish Gupta, Nitendra Rajput, Krishna Kummamuru, Raymond Rudy, Brent Miller, Jane Xu, Steven Wysmuller, Alberto Giacomel, Vinod A Bijlani, Pankaj D Lunia, Tran Viet Huan, Wei Xiong Shang, Chen WC Wang, Bob Schloss, Rosario Usceda-Sosa, Anton Riabov, Magda Mourad, Alexey Ershov, Eitan Israeli, Evgenia Gyana R Parija, Ian Simpson, Jen-Yao Chung, Kohichi Kajitani, Larry L Light, Lisa Amini, Marco Laumanns, Mary E Helander, Milind Naphade, Sebastien Blandin, Takayuki Osogami, Tony R Heritage, Ulysses Mello, Wei CR Ding, Wei CR Sun, Xiang XF Fei, Yu Yuan, Bipin Joshi, Vishalaksh Agarwal, Pallan Madhavan, Ravindranath Kokku, Mukundan Madhavan, Rashmi Mittal, Sandeep Sandha, Sukanya Randhawa, Karthik Vishweshvariah, Guruduth Banavar

For discussions, ideas and contributions. Apologies to anyone unintentionally missed. Material gratefully taken from multiple sources. Apologies if any citation is unintentionally missed. Tutorial on 27 July 2015 @ IJCAI 2015 4

Outline

�  Motivating Examples �  Basics

¡  AI: Analytics to process data, derive insights and enable action ¡  Smart City

÷  Challenges ÷  Innovation needs – value desired ÷  Critical considerations different from other applications

¡  Open Data ÷  Introduction and issues ÷  Giving semantics for evolution

¡  Access via APIs �  Applications

¡  Open data as disruptor technology: ÷  patents, corruption, citizen engagement

¡  Health ¡  Environment Pollution ¡  Transportation ¡  Tourism

�  Discussion


Examples

Tutorial on 27 July 2015 @ IJCAI 2015 6


[India] Ganga – Local Ground Situation @ Varanasi (Assi/ Tulsi Ghats) + Patna

Photos of/ at Assi/ Tulsi Ghat, Varanasi on 25 March 2015 during 1700-1800 Hrs

Assi Ghat post recent cleanup Bathing on Tulsi Ghat

A nullah draining into Ganga A manual powered boat

Photos at Gandhi Ghat, Patna on 18 March 2015 during 1700-1800 Hrs

Example –River Water Pollution

�  Value – To individuals, businesses, government institutions ¡  Example – Can I take a bath? Will it cause me dysentery? ¡  Example – How should govt spend money on sewage treatment for maximum

disease reduction? �  Data – Quantitative as well as qualitative

¡  Dissolved oxygen, ¡  pH, ¡  … 30+ measurable quantities of interest

�  Access – ¡  Today, little, and that too in water technical jargon ¡  In pdf documents, website

Key Idea: Can we make insights available when needed and help people make better decisions?


We All See Traffic Daily. An Illustration from Across the Globe

Source: Google map for New York City and New Delhi; Search done on Aug 20, 2010

Characteristics New York City, USA

New Delhi, India

Beijing, China Moscow, Russia Ho Chi Minh City, Vietnam

Sao Paolo, Brazil

1 How is traffic pre-dominantly managed

Automated control, manual control

Manual control

Automated control, manual control

Automated, manual control

Manual control Automated, manual control, Rotation system (# plate based)

2 How is data collected Inductive loops, cops, video, GPS

Traffic surveys, cops

Video, GPS, cops GPS, some video, cops

Traffic surveys, cops Video, GPS, cops

3 How can citizens manage their resources

GPS devices, alerts on radio, web, road signs (variable)

Alerts on radio

alerts on radio, road signs (variable), mobile alerts

GPS, radio, road signs, mobile alerts

Alerts on radio GPS devices, alerts on radio, web

4 Traffic heterogeneity by vehicle types(Low: <10; Medium 10-25; High: >25)

Low High Low Low Medium Low

5 Driving habit maturity (Low: <10 yrs; Medium: 10-20; High: > 20)

High Low Low Low Low Medium

6 Traffic movement Lane driving Chaotic Lane driving Lane driving Chaotic Lane Driving 9

Example –Traffic Management

�  Value – To individuals, businesses, government institutions ¡  Example – Can I reach office on time? Where to park if I take my car? ¡  Example – How much overt-time does the city need to give today? Where

should I deploy my traffic cops today? ¡  Example – When to service city’s buses?

�  Data – Quantitative as well as qualitative ¡  Volume – traffic count ¡  Speed on road ¡  City events

�  Access – ¡  Today, little and on city websites ¡  Facebook sites



Basics: AI


Advanced AI Techniques (Analytics) like Planning & Machine Learning make use of data and models to provide insight to guide decisions

Models

Analytics

Data

Insight

Data sources: Business automation

Instrumentation Sensors

Web 2.0 Expert knowledge

“real world physics”

Model: a mathematical or

algorithmic representation of

reality intended to explain or predict some aspect of it

Decision executed automatically or

by people


Example: Tutorials

�  Are they useful? (Descriptive) ¡  Answering needs an assessment about the event

�  If it happens next time, how many will attend? (Predictive) ¡  Above + Answering needs an assessment about unknowns

(e.g., future) �  Should you attend? (Prescriptive)

¡  Above + Answering needs understanding the goals and current status of the individual


Analytics Landscape

Degree of Complexity

Com

petit

ive

Adv

anta

ge

Standard Reporting

Ad hoc reporting

Query/drill down

Alerts

Simulation

Forecasting

Predictive modeling

Optimization

What exactly is the problem?

What will happen next if ?

What if these trends continue?

What could happen…. ?

What actions are needed?

How many, how often, where?

What happened?

Stochastic Optimization

Based on: Competing on Analytics, Davenport and Harris, 2007

Descriptive

Prescriptive

Predictive

How can we achieve the best outcome?

How can we achieve the best outcome including the effects of variability?


Real-World Applications of ICT Follow a Pattern

n Value (from Action, Decisions) – Providing benefits that matter, to people most in need of, in a timely and cost-efficient manner. Going beyond technology to process and people aspects.

n Data + Insights – Available, Consumable with Semantics, Visualization / Analysis

n Access - Apps (Applications), Usability - Human Computer Interface, Application Programming Interfaces (APIs)


ML Reference

�  WEKA ¡  Website: http://www.cs.waikato.ac.nz/~ml/weka/index.html ¡  WEKA Tutorial:

÷  Machine Learning with WEKA: A presentation demonstrating all graphical user interfaces (GUI) in Weka.

÷  A presentation which explains how to use Weka for exploratory data mining. ¡  WEKA Data Mining Book:

÷  Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)

÷  http://www.cs.waikato.ac.nz/ml/weka/book.html ¡  WEKA Wiki: http://weka.sourceforge.net/wiki/index.php/Main_Page

�  Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd ed. �  http://www.kdnuggets.com/2015/03/machine-learning-table-elements.html


Basics: Smart City


What is a Smart City?


Smart city can mean one or more of the following: �  As a resource optimization objective, it is to know and manage a

city's resources using data.

�  As a caring objective, it is about improving standard of life of citizens with health, safety, etc indices and programs.

�  As a vitality objective, it is about generating employment and doing sustainable growth.

A city leadership can choose among these or define their own objective(s) and manage with measurements to pro-actively achieve it

18

See other FAQs at: https://sites.google.com/site/biplavsrivastava/research-1/intelligent-systems/scfaqs


Cities are traditionally built and governed by independent departments operating as domains of functions

C i t y

I n f r a s t r u c t u r e

D a t a

Water Energy Transport Security Planning Food . . . Science Health ICT

City

Responsibility

Department

Responsibility

Project

Responsibility

Task

Responsibility

Typically lacking holistic view

Ope

rati

onal

Sys

tem

s Before


D o

IT

An integrated Smarter City Framework – a comprehensive management system across all core systems, will anchor the vision to executable steps

I n f r a s t r u c t u r e

D a t a

City

Responsibility

Department

Responsibility

Project

Responsibility

Task

Responsibility

Ope

rati

onal

Sys

tem

s

C i t y M a n a g e m e n t Analytics, Insight, Visualization, Control Center, etc.

Water Energy Transport Security Planning Food . . . Science Health . . .

D o

W

D o

E

D o

T

D o

S

D o

P

D o

F

D o

. . .

D o

S

D o

H

. . .

B u s i n e s s P r o c e s s e s a n d A p p l i c a t I o n s

Your City

After


Smarter Cities solution paths leverage a similar approach

Uni

que

valu

e re

aliz

ed

Use of Smarter Cities capabilities

ManageData 1

AnalyzePatterns 2

Optimize Outcomes 3

Integrate service information to improve department operations

Develop integrated view to improve outcomes and compliance

Leverage end-to-end case management to optimize service delivery

Ç Improve service levels È Reduce fraud and abuse

Ç Focus on the citizen Ç Savings from overpayment Ç Assistance with compliance

Ç Integrated case management Ç Automation of citizen support È Reduce operating costs

Basics: Open Data


Open Data

�  Open data is the notion that data should not be hidden, but made available to everyone. The idea is not new.

�  Scientific publications follow this: “standing on the shoulders of giants” ¡  Science stands for repeatability of results and

hence, sharing ¡  The scientific community asserts that open

data leads to increased pace of discovery. (See: Ray P. Norris, How to Make the Dream Come True: The Astronomers' Data Manifesto, At http://www.jstage.jst.go.jp/article/dsj/6/0/6_S116/_article, Accessed 2 Apr, 2012)

�  Governments are the new source for open data ¡  Data.gov efforts world-wide; 400+

governmental bodies, including 20+ national agencies, including India, have opened data

¡  In India, additional movement is “Right to Information Act”


Not to Be Confused With Orthogonal Trend – Big Data

�  Volume �  Variety �  Velocity �  Veracity �  …

Cartoon critical of big data application, by T. Gregorius. http://upload.wikimedia.org/wikipedia/commons/thumb/b/b3/Big_data_cartoon_t_gregorius.jpg/220px-Big_data_cartoon_t_gregorius.jpg


400+Data Catalogs of Public Data

As on 21 July 2015


Data.gov (USA)

As on 16 June 2015 Tutorial on 27 July 2015 @ IJCAI 2015

26

City Level – Chicago, USA

Tutorial on 27 July 2015 @ IJCAI 2015 27 As on 16 June 2015

Data.gov.in (India)

As on 16 June 2015 Tutorial on 27 July 2015 @ IJCAI 2015

28

City Level – Buenos Aires, AR

Tutorial on 27 July 2015 @ IJCAI 2015 29 As on 21 July 2015

Peek into the Future - Amsterdam

http://citydashboard.waag.org/ Tutorial on 27 July 2015 @ IJCAI 2015 30

Illustration of Levels

Source: http://5stardata.info/

Does Opening Data Make It Reusable? No

1

2

3

4

5


Linking of Open Data for Reusability

32

Source: http://5stardata.info/

Source: http://lab.linkeddata.deri.ie/2010/star-scheme-by-example/


India: Right to Information Act

�  Any citizen “may request information from a "public authority" (a body of Government or "instrumentality of State") which is required to reply expeditiously or within thirty days.” ¡  Passed by Parliament on 15 June 2005 and came fully into force on 13

October 2005. Citation Act No. 22 of 2005 �  Lauded and reviled

¡  Brought transparency ¡  Also,

÷  Increased bureaucracy ÷  Shortcomings in preventing corruption

�  More information ¡  http://en.wikipedia.org/wiki/Right_to_Information_Act ¡  http://rti.gov.in


Data Quality in Public Data in India

� Right to Information ¡  Not even 1* ¡  Information available to requester, but no one else

� Data.gov.in ¡  2-3* ¡  Available in CSV, etc but not uniquely referenceable

� Open data movements are moving to linked data form for semantics


Semantics for Published Data

35

Classify data in public domain. Use schema.org as illustration.

¡  Select an area (e.g., food, news events, crime, customs, diseases, …) ¡  Build + disseminate the catalog tags via a website ¡  Encourage publishers to use meta-data tags and enable search

Catalog/ ID

General Logical

constraints

Terms/ glossary

Thesauri “narrower

term” relation

Formal is-a

Frames (properties)

Informal is-a

Formal instance

Value Restrs. Disjointness, Inverse, part-of…

Tutorial on 27 July 2015 @ IJCAI 2015 Credits: Ontologies Come of Age McGuinness, 2001 From AAAI Panel 99 – McGuinness, Welty, Uschold, Gruninger, Lehmann Plus basis of Ontologies Come of Age – McGuinness, 2003

�  Abstract: This document describes a core ontology for organiza7onal structures, aimed at suppor7ng linked-‐data publishing of organiza7onal informa7on across a number of domains. It is designed to allow domain-‐specific extensions to add classifica7on of organiza7ons and roles, as well as extensions to support neighbouring informa7on such as organiza7onal ac7vi7es.

1. Introduc7on 2. Conformance 3. Namespaces 4. Overview of ontology 5. Design notes 6. Notes on style 7. Organiza7onal structure

7.1 Class: Organiza7on 7.1.1 Property: subOrganiza7onOf 7.1.2 Property: transi7veSubOrganiza7onOf 7.1.3 Property: hasSubOrganiza7on 7.1.4 Property: purpose 7.1.5 Property: hasUnit 7.1.6 Property: unitOf 7.1.7 Property: classifica7on 7.1.8 Property: iden7fier 7.1.9 Property: linkedTo

7.2 Class: FormalOrganiza7on 7.3 Class: Organiza7onalUnit 7.4 Notes on formal organiza7ons 7.5 Notes on organiza7onal hierarchy 7.6 Notes on organiza7onal classifica7on

8. Repor7ng rela7onships and roles 8.1 Class: Membership

8.1.1 Property: member 8.1.2 Property: organiza7on 8.1.3 Property: role 8.1.4 Property: hasMembership 8.1.5 Property: memberDuring 8.1.6 Property: remunera7on

8.2 Class: Role 8.2.1 Property: roleProperty

8.3 Property: hasMember 8.4 Property: reportsTo 8.5 Property: headOf 8.6 Discussion

9. Loca7on 9.1 Class: Site

9.1.1 Property: siteAddress 9.1.2 Property: hasSite 9.1.3 Property: siteOf 9.1.4 Property: hasPrimarySite 9.1.5 Property: hasRegisteredSite 9.1.6 Property: basedAt

9.2 Property: loca7on 10. Projects and other ac7vi7es

10.1 Class: Organiza7onalCollabora7on 11. Historical informa7on

11.1 Class: ChangeEvent 11.1.1 Property: originalOrganiza7on 11.1.2 Property: changedBy 11.1.3 Property: resultedFrom 11.1.4 Property: resul7ngOrganiza7on

A. Change history B. Acknowledgments C. References

C.1 Norma7ve references C.2 Informa7ve references http://www.w3.org/TR/vocab-org/ Tutorial on 27 July 2015 @ IJCAI 2015

Illustration: W3C Organization

36

Usage of W3C’s Org Ontology – Community Directory

@prefix skos: <http://www.w3.org/2004/02/skos/core#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . @prefix vcard: <http://www.w3.org/2006/vcard/ns#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix dir: <http://dir.w3.org/directory/schema#> . @prefix directory: <http://dir.w3.org/directory/orgtypes/> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix gr: <http://purl.org/goodrelations/v1#> . @prefix org: <http://www.w3.org/ns/org#> . <> foaf:primaryTopic <#org> . <#org> a org:Organization, dir:Organization, gr:BusinessEntity, vcard:Organization ; rdfs:label "International Business Machines" ; gr:legalName "International Business Machines" ; vcard:organization-name "International Business Machines" ; skos:prefLabel "International Business Machines" ; dir:isOrganizationType directory:commercial ; vcard:url <http://www.ibm.com> ; vcard:logo <http://upload.wikimedia.org/wikipedia/commons/thumb/5/51/IBM_logo.svg/200px-IBM_logo.svg.png> ; rdfs:comment """International Business Machines Corporation (NYSE: IBM), or IBM, is an American multinational technology and consulting corporation, with headquarters in Armonk, New York, United States. IBM manufactures and markets computer hardware and software, and offers infrastructure, hosting and consulting services in areas ranging from mainframe computers to nanotechnology.""" . <#org> org:siteAddress <#address-1NewOrchardRoad+Armonk+UnitedStates> . <#address-1NewOrchardRoad+Armonk+UnitedStates> a vcard:VCard, vcard:Address ; vcard:street-address "1 New Orchard Road " ; vcard:locality "Armonk " ; vcard:country-name "United States" ; vcard:region "New York" ; vcard:postal-code "10504-1722" .


Still Confused on Semantics? Start with Linked Data Glossary


Open Data References

�  Concept ¡  Open Data, At http://en.wikipedia.org/wiki/Open_data, ¡  Open 311, At http://open311.org/ ¡  Catalog of Open Data, At http://datacatalogs.org/dataset ¡  Data City Exchange: http://www.imperial.ac.uk/digital-city-exchange

�  India specific ¡  Open data report in India, At http://cis-india.org/openness/publications/ogd-report

�  Standards ¡  W3C, At http://www.w3.org/2011/gld/ ¡  5 Star Linked Data ratings, At http://www.w3.org/DesignIssues/LinkedData.html

�  Applications and ecoystems ¡  Introduction to Corruption, Youth for Governance, Distance Learning Program, Module 3, World Bank

Publication. Accessed on June 15th 2011, At http://info.worldbank.org/etools/docs/library/35970/mod03.pdf

¡  Dublinked, At http://dulbinked.ie


Basic: Access via APIs


Business

Source: Bessemer Venture Partners 2012

Business Capabilities as Services are being via APIs and delivered as-a-service, allowing Businesses to engage with Clients and Partners with speed at Scale

41 Tutorial on 27 July 2015 @ IJCAI 2015 41

Example: API Registry

42

As on 16 July 2015

API Example http://www.programmableweb.com/api/sabre-instaflights-search


A Composition (Mashup) Example


REST v/s Web Services?

45

REST •  support limited integration styles, and

involves fewer decisions on architectural alternatives

•  This simplifies client-side integration steps (at the cost of lessening automation in system evolution); more focus on do-it-yourself

Source: Pautasso et al, RESTful Web Services vs. “Big” Web Services: Making the Right Architectural Decision, WWW 2008 45

Example: Open 311 (http://open311.org/)


�  Refers to non-emergency events like graffiti, garbage, down trees, abandoned car, … ¡  Not human life threatening ¡  60+ cities support it world-wide

46

Scaling with Open 311


Discovering Open 311 of a City


�  http://311api.cityofchicago.org/open311/discovery.json �  Result

{"changeset":"2012-09-14T08:00:00-05:00”, "contact":"Contact [email protected] for assistance", "key_service":"Visit http://test311api.cityofchicago.org/open311 to request an API Key", "endpoints": [{"specification":"http://wiki.open311.org/GeoReport_v2", "url":"http://311api.cityofchicago.org/open311/v2", "changeset":"2012-09-14T08:00:00-05:00”, "type":"production","formats":["text/xml","application/json"]}, {"specification":"http://wiki.open311.org/GeoReport_v2", "url":"http://test311api.cityofchicago.org/open311/v2", "changeset":"2012-09-14T08:00:00-05:00” , ”type”:"test","formats":["text/xml","application/json"]}]}

48

Demonstration: Open 311


�  List of services ¡  http://311api.cityofchicago.org/open311/v2/services.json ¡  Result

[{"service_code":"4ffa4c69601827691b000018","service_name":"Abandoned Vehicle","description":"Abandoned vehicles are taken to auto pound 3S or 3N where they are -- if not redeemed by the owners -- sold for scrap.","metadata":true,"type":"batch","keywords":"code:SKA","group":"Streets & Sanitation"}, {"service_code":"4ffa9cad6018277d4000007b","service_name":"Alley Light Out","description":"One or more alley lights out, on a wooden pole in the alley itself, are reported under this service request type. Important information needed when reporting alley lights out includes: the exact address that the light/lights are behind, how many lights are out, and if the light(s) are completely out or if they blink on and off intermittently. Alley light repairs are done during the day when the lights are not on, so this information is essential to expedite the repair work.","metadata":true,"type":"batch","keywords":"code:SFA","group":"Transportation"},

…]

�  Details of a service ¡  http://311api.cityofchicago.org/open311/v2/services/4ffa4c69601827691b000018.json ¡  Result

{"service_code":"4ffa4c69601827691b000018", "attributes": [{"variable":true,"code":"FQSKA1", "datatype":"singlevaluelist","required":false,"order":1, "description":"Vehicle Make/Model", "values": [{"key":"ASVEAV","name":"(Assembled From Parts,Homemade)"}, {"key":"HOMDCYL","name":"(Homemade Motorcycle, Moped.Etc.)"}, {"key":"HMDETL","name":"(Homemade Trailer)"}, …] ...]}

49

Demonstration: Open 311


�  http://311api.cityofchicago.org/open311/v2/services/4ffa9cad6018277d4000007b.json �  Result

{"service_code":"4ffa9cad6018277d4000007b", "attributes": [{"variable":true,"code":"ISTHELI2", "datatype":"singlevaluelist","required":true,"order":1, "description":"Is the light located in your alley or the street?", "values":[{"key":"ALLEY","name":"Alley"}, {"key":"STREET","name":"Street"}]},

{"variable":true,"code":"POLEWORM", "datatype":"singlevaluelist","required":true,"order":2, "description":"Is the pole wooden or metal?", "values":[{"key":"METAL","name":"Metal"}, {"key":"WOODEN","name":"Wooden"}]}, {"variable":true,"code":"ISTHELI3", "datatype":"singlevaluelist","required":true,"order":3, "description":"Is the light directly behind this address?", "values":[{"key":"NO","name":"No - Light Not Directly Behind Address"} ,{"key":"YES","name":"Yes - Light Directly Behind Address"}]}, {"variable":true,"code":"A511OPTN", "datatype":"string","required":false, "datatype_description":"Enter number as 999-999-9999","order":4, "description":"Input mobile # to opt-in for text updates. If already opted-in, add mobile # to contact info."}]}

50

Chicago: Service Tracking


Example: Application over Open Data (Chicago)


Example of API Design– APIs for Temperature at Conference Location

�  API examples ¡  Get temperature (input: current, last, input instant) ¡  Get temperature interval (input: day) ¡  Get average temperature (input: time range)

�  REST or web-service �  Semantic annotation on input and output


Every citizen is a potential city event sensor •  Citizen notices 311 event worth reporting •  Reports event using mobile

•  Launches mobile application •  Browses recent already-reported events •  Creates new event report

•  [Is pre-enabled or gets any needed credentials to report event] •  Identifies service type for new event •  Shares location using mobile device (coordinates) •  Can add location annotations (road, district, city) and description

•  Get confirmation of submission •  Get updates on service request

Extreme Personalization

=

Location Intelligence

Empowered Citizen

+

SocialAnalytics

+ +

ALLGOV SCENARIO: CROWDSOURCING 311* EVENT REPORTING


Browsing Services in One’s City: Mary M. can look at the 311 services her city provides On selecting the icon, •  She sees a small set of categories

(health, building, traffic, cityimage, others) around which all the city’s services are grouped.

•  She can look at a list of services and check out the agencies involved

•  If there has been a change in agency responsible or new services added for an agency, she can note that directly

Browsing Services in Other Cities: Her colleagues from another city are visiting. She may want to bring a window (instantiate an app with browse city pattern) to look at what that city offers to their citizens [Alternatively, if she is travelling to another city, she may be interested to know how that city does compared to her’s, by which agency, etc.] On selecting the icon, •  See sees a small set of familiar categories (health, building, traffic,

cityimage, others) regardless of what the city calls its services •  She can look at a list of services and check out the agencies

involved

If her city does something different, she can show that to her colleagues in her or other cities.


A Demonstration of AllGov Pattern with Open 311


Applica7on Pa\ern

¡ What is it?: A pa\ern is any applica7on using APIs, with some informa7on generalized (i.e., removed and parameterized)

¡ Business Value: A pa\ern ÷ standardizes the usage experience by promo7ng similar behavior (for users) ÷ simplifies applica7on development by templa7zing API interac7ons (for developers)

÷ serves as the organiza7on’s memory of the best-‐prac7ces in developing a class-‐of-‐applica7ons even when the specific APIs may not be relevant (for business)

¡ Key Technical Issue ÷ What pa\erns should one build ? Theore7cally, there exists a trivial method to blindly generate a pa\ern from any applica7on. Any pa\ern development process has to do be\er than this baseline.

÷ How should the pa\erns be used in prac7ce? ÷ Building a tool-‐enabled process around Pa\ern-‐based programming


Applica7on Pa\ern

¡ Approach followed in AllGov ÷  Common steps taken by a role player is a candidate pa\ern ÷  Common steps that can be executed in the same infrastructure is a candidate pa\ern

¡  Pa\ern 1: Browse city services pa\ern [User Role: Govt. Dept Admin; Environment: PRODUCTION system] ÷  find a city's services ÷  find a service's defini7on ÷  find services of a par7cular high-‐level category (example: building, graffi7, ...)

¡  Pa\ern 2: Create service request pa\ern [User Role: Developer; Environment: TEST system] ÷  Browse city services ÷  Browse raised city service requests ÷  Create a new service request

¡  Pa\ern 3: Create service request pa\ern [User Role: General ci7zen of a par,cular City; Environment: PRODUCTION system]

÷  Browse city services ÷  Browse raised city service requests ÷  Create a new service request


AllGov Scenario Deconstruction (flows)

Customer Mobile

AllGov City Services

1

2

External IBM Client

browse events get recent events

Request confirmation

get service types create request

Post location coordinates

Post details on Event, location

3 Notify service completed

P1, P1+

P2, P3


Video Demonstration - AllGov


Applications with Open Data


Open Data as Disruptor Technology


Is happening in areas where information can disrupt status-quo �  Granting and Defending Patents �  Detecting Corruption �  Citizen Engagement

62

Patents


�  Are for novel, useful and non-obvious ideas �  Which have not been known read (read: published

and available in public domain)

63

IP (Patent) Grants and Defense


Example: Nutmeg


http://www.tkdl.res.in/

Corruption


Corruption - “the misuse of public office for personal gains”

* Source: http://cpi.transparency.org/cpi2012/results/

Corruption afflicts both public and corporate services world wide. It is known that it has a significant negative impact on the growth of economies and hence, is universally considered undesirable.

Corruption : “Monopoly + Discretion – Accountability” (Klitgaard, Robert E. Controlling corruption. Berkeley: U. of California Press, 1988)


A Nation’s Competitiveness and Corruption Perception

Don’t Go Hand-in-Hand

For Promoting Growth, Corruption Perception has

to be Removed

Latin America’s Competitiveness


Source: http://americasmi.com/en/expertise/articles-trends/page/the-cost-of-corruption-to-latin-americas-competitiveness

Some Key Questions Related to Corruption

�  Exchange of money: can a service for which the customer does not pay a fee (free service) be termed corrupt? Or conversely, can a corrupt practice only happen if the customer pays for a service?

�  Human agents: can a service be corrupt if the agent delivering the service is not a human but an automated agent?

�  Contention for resources: can corruption happen if delivering it requires no contention of resources? Alternatively, if resources are scarce, will an objective way of allocating them help remove corruption?


Metamodel – Expressing Key Concepts for Corruption

Provider

Ac7vity Process

Task Decision Inputs Outputs Escala7on

Requestor

0..1 *

1 +

Person

Organiza7on

1

1

1

1

1 1 1

* Process Instance *

Ac7vity Instance

1

+

Execu7on Time

Execu7on Cost

1

1 1

1

1


A Computational Model for Corruption Assessment, Nidhi Rajshree, Nirmit V. Desai and Biplav Srivastava, IJCAI 2013 Workshop on Semantic Cities, Beijing, 2013

Framework Evaluation, by Example National Registration - Kenya

1. Submit supporting documents

2. Validate

docs

4. Handover serialized App Form

11. App signed and stamped by

Chief Asst. Officer

12. Submit documents to

NRB

13. Verify identity of the

applicant

14. Process ID Card

17. Collect

ID Card

- Proof of birth - Proof of citizenship - Proof of residence

5. Fill and submit application form

- Form 101 - Form 136 A - Form 136 C

6. Take finger prints

7. Click photograph for

ID card

8. Handover the waiting card

10. Submit documents

to Chief

3. Vetting

15. Send ID card to the

Registration Office - Additional proof of

residence

Ancestral home town is a border district or age >> 18

Insufficient documents

Sufficient documents

9. Receive waiting card and wait for processing

16. Receive ID Card from

NRB

Citiz

en

Reg

istr

atio

n O

ffice

r

Satisfied

Not satisfied

Vett

ing

Com

mitt

ee

Ch. A

sst.

Offi

cer

NR

B O

ffice

r

National Registration

Kenya India (Aadhar) USA (Social Security)

•  The decision node, 3 - vetting, and the activity, 13 - verify identity, are discretionary with no clear mechanism on how to accomplish them.

•  In contrast, the checks for documents having been submitted are objective.

•  There is no Service Level Agreement (SLA) for the process.

•  The ID process is monopolistic since only a single authority

•  (registration office) can process it. •  The process has little reviewability

and low visibility since there is no escalation mechanism.

•  18 Proofs of Identity (PoI) and 33 Proofs of Address (PoA) documents are permitted for making the request.

•  The process also allows discretion by allowing at- tested documents from high-level officials.

•  The cost and time limits for the service are prescribed.

•  The process, however, can only be handled by a single agency creating a monopoly.

•  In SS, a clear list of documents proving US citizenship (or legal residence), age and identity is listed.

•  There is little room for discretion because no category allows a signed attestation by a high-level official to be acceptable

•  The cost and time limits for the service are prescribed.

•  The process, however, can only be handled by a single agency creating a monopoly.


Framework Evaluation, by Example

International Driving Permit (IDP)

1. Submit supporting documents

2. Validate

docs

5. Handover Appl Form

10. Stamp and sign

the IDP

13. Collect IDP

- Driver’s license - Passport - Air tickets - VISA

5. Fill and submit application form

- Form CMV1

+ 4. DL Address change process

8. Verify

applicants driving skills

DL address not under RTO jurisdiction

Insufficient documents

DL address under RTO jurisdiction

Citiz

en

Fron

t Des

k O

ffice

r

Satisfied

Not satisfied

Insp

ecto

r

Reg

iona

l Tr

ansp

ort

Offi

cer

3. Validate address

7. Send applicant for DL Test

6. Verify DL issuance

date

9. Send application to Regional Transport

Officer

11. Send IDP to front

desk officer

12. Receive IDP from Regional

Transport Officer

Address has not changed

DL issued within 3 months

Address has changed

DL issued within more than 3 months


International Driving License

India (IDP) USA (AAA) •  Service execution cost is specified

(of Rs 500) but not service execution time given.

•  There is no escalation mechanism •  The check whether all documents

have been submitted is objective. •  The IDP is monopolistic since only

a single authority (RTO) can process it.

•  The process has little reviewability and low visibility since there is no escalation mechanism.

Procedure involves filling a form online, visiting the office of an authorized agency with a valid state-issued driver’s license, photos and fees, and getting the permit. Here, there are multiple agencies to process the request and the prerequisite driver license can be verified objectively (e.g., with social security databases). •  No monopoly •  Objective criteria


Tackling Corruption

Tackling corruption pro-actively: �  Open Government Data

¡  Increases transparency hence increasing the risk of being caught (i.e., increasing accountability) in the act of corruption

¡  Makes benchmarking by Service Level Agreements (SLAs) possible

�  Process Redesign ¡  Ensures a robust process design reducing corruption hotspots ¡  Formalizes adequate data needs, reduces monopoly & discretion

�  Automation ¡  Automation needs outcomes and inputs to be formally defined ¡  Reduces discretion, forces data formalization (input, output, outcome)

Corruption : “Monopoly + Discretion – Accountability” (Klitgaard, Robert E. Controlling corruption. Berkeley: U. of California Press, 1988)


Corruption – It’s All Around


Citizen Engagement


�  Reporting problems �  Finding help �  Generally: People-as-sensors

78

Chicago: Food Poisoning


http://www.foodbornechicago.org/

Hottest Trend in Public Health


Health

Details: Africa (2014-), India (2013-)


Two Tales from (Public) Health

Cutting-edge Technical Progress •  Enormous improvement in our

understanding of diseases. E.g., Computational epidemiology

•  Enormous advances in treating diseases are being made ÷  We are living longer - A baby girl born

in 2012 can expect to live an average of 72.7 years, and a baby boy to 68.1 years. This is 6 years longer than the average global life expectancy for a child born in 1990. (Source: WHO 2014 Health Statistics)

•  Data on disease outbreaks is more available than ever before thanks to open data movement (E.g., data.gov, data.gov.in)

Stone-age Ground Reality �  Half of the top 20 causes of deaths

in the world are infectious diseases, and maternal, neonatal and nutritional causes, while the other half are due to noncommunicable diseases (NCDs) or injuries. (Source: WHO 2014 Health Statistics)

�  Worse – Indifference, mismanagement in response to communicable diseases - late response to known diseases, in known period of the year ¡  E.g.: Japanese Encephalitis (JE) has been

prevalent for ~3 decades in some parts of India killing 600+ every year

¡  District level health experience is not reused over time and in similar regions


Ebola Data

Crowd sourced

Online

National Government

International Bodies


Case Study: Dengue (Mosquito-borne) �  Overall cost of a Dengue case is US$ 828 (Sabchareon et al 2012). �  From 9 countries in 1960s, it has spread to more than 110 countries now

�  Prevention methods COMMUNITY 1.   Mosquito Coils & Candles: The use of mosquito coils, candles & vapor mats indoors and outdoors of homes to combat

mosquitoes. 2.   Window screens & Bed Nets: The use of window screens in homes and bed nets in bedrooms to keep mosquitos out. 3.   Insecticide Application: Application of insecticide to kill mosquitos that invade homes and surrounding areas. 4.   Larviciding at Home: Application of larvicide in homes to kill larvae that live in stagnant water breeding sites like small

ponds, gutters, cisterns, barrels, jars, and urns. 5.   Household/Community Cleanup: Organize cleanups within communities in the surrounding housing areas and

individual homes to recycle potential breeding sites like discarded plastic bottles, cans, old tyres, and any trash that can hold water for mosquitoes to breed in.

GOVERNMENT 6.   Surveillance For Mosquitoes: Conduct periodical surveillance in hotspot areas and other communities to look for signs of

mosquitoes. 7.   Medical Reporting: To collate and compile reports of dengue cases and statistics to prioritize and focus dengue and vector

mosquito control efforts and actions for best results. 8.   Effective Publicity & Campaigns: To foster and champion effective campaigns amongst communities and create adequate

public awareness of combating dengue. 9.   Enforcement: Support and enforce the public and communities to practice effective dengue vector elimination under

existing laws and implement new laws as appropriate for public health. 10.  Insecticide Fogging: Conduct fogging in areas that have mosquitoes and dengue outbreak hotspots to kill adult mosquitoes. 11.   Public Education: Foster, promote, and participate in public education in schools and all possible public meeting places to

inform communities how to eliminate dengue vector mosquitoes, recognize early symptoms of the disease, and proper medical care and reporting.

CORPORATE 12.  Education: To undertake community service initiatives and campaigns through marketing expertise and the media of TV,

radio, and newspapers. 13.  PR/CSR: To use public relations and customer service relations to reach communities on the fight against dengue. 14.  Adult Mosquito Traps: To provide adult mosquito traps and other measures within the work areas to protect employees

and workers from mosquitoes bites that transmit dengue. 15.   Mosquito Repellants: Provide mosquito repellants to employees and workers within the work areas for further protection. 16.   Mosquito Control Materials, Methods, and Agents: To provide the tools to the public and government that are

necessary for dengue mosquito vector control like pesticides, biocontrol agents, mosquito traps, repellants, and other means to prevent dengue by eliminating the mosquito vectors.

WHO, 2013, Dengue Control. At http://www.who.int/Denguecontrol/research/en/, Accessed 21 June 2013. Entogenex, 2013, Integrated Mosquito Management. At http://www.entogenex.com/what-is-integrated-mosquito- management.html, Accessed 21 June 2013. Tutorial on 27 July 2015 @ IJCAI 2015 84

So, Do We Control Dengue

Effectively? NO

Source: http://nvbdcp.gov.in/den-cd.html

Data for India •  Increasing

number of states every year

•  No consistent reduction of cases

1"

10"

100"

1000"

10000"

100000"

C" C" C" C" C" C"

2008" 2009" 2010" 2011" 2012" 2013*"

Andhra"Pradesh"

Arunachal"Pradesh"

Assam"

Bihar"

Cha9sgarh"

Goa"

Gujarat"

Haryana"

Himachal"Pd."

J"&"K"

Jharkhand"

Karnataka"

Kerala"

Madhya"Pd."

Meghalaya"

Maharashtra"

Manipur"

Mizoram"

Nagaland"

Orissa"

Punjab"

Rajasthan"

Sikkim"

Tamil"Nadu"

Tripura"

UPar"Pradesh"

UPrakhand"

West"Bengal"

A&"N"Island"

Chandigarh"


(ROI) Metrics

�  Expense for disease control ¡  $/person spent: How much money (in $) is spent for a given method divided by the population

of the region. Lower is better.

�  Impact of a disease control method ¡  Reduction: What is the magnitude of reduction in disease cases due to a method, expressed as

a percentage, in a time period (e.g., year, disease season)? Higher is better. ¡  Cases/ person: How many reported cases of a disease occurred in a time period divided by the

population of the region when a method was adopted? Lower is better.

�  Cost-effectiveness: ¡  Cases / $: how many cases were reported for a disease per dollar spent on controlling it in a

given time period? Lower is better.


Major Methods to Tackle Dengue

�  M1: Public awareness campaigns: to prevent conditions conducive to disease propagation, to improve reporting

�  M2: Chemical Control: Aerosol space spray �  M3: Biological Control: Use of biocides �  M4: Distributing equipments: bednets, insecticide-

treated curtains �  M5: Vaccination against the disease


Dengue Control Case Studies from Literature

88

•  An approach may use 1 or more method(s)

•  They incur different costs per person

•  Their efficacy is subject to various factors

Still, can we reuse these results in new areas?

Tutorial on 27 July 2015 @ IJCAI 2015 88 Details:

Vandana Srivastava and Biplav Srivastava, Towards Timely Public Health Decisions to Tackle Seasonal Diseases With Open Government Data , International Workshop on the World Wide Web and Public Health Intelligence (W3PHI-2014), AAAI 2014

Challenge: Prescribe Methods to Use for a Hypothetical, Illustrative Area - Sundarpur

�  City is Sundarpur ¡  Made up of 10 districts ¡  10,000 people in each district.

�  Disease control ¡  Each district allocates $10,000 per annum to prevent disease. ¡  The city has a district-level health administrator per district and then an

overall citywide public health administrator.

�  What approach/ method should the district health officer use? What should the city health officer recommend? ¡  a mix of control methods to produce the maximum reduction feasible. ¡  Default option is to do nothing. This is unfortunately followed a lot!


Cost-benefits for Different Approaches

90

* represents assumption made to compensate for missing data.


Prescription for Sundarpur

�  Best tactical option for administrators at Sundarpur (at district and the whole city level) ¡  is O1_A1 since it brings the maximum reduction. ¡  If the administrators are interested to cover the maximum number of people in the given

budget, the best method is still O1_A1. ¡  If the administrators are interested to show maximum reduction in cases for a pocket of the

city (sub- district level which may be more prone to the disease), they may choose O4_A4 but it costs maximum and thus can be perceived as taking resources away from the not- directed areas.

�  Strategic option ¡  Select top-2 (O1_A1 and O2_A2), and try them in 5 districts each in one year. It hedges risk of

variability between Sundarpur and old location of previous studies. ¡  Based on efficacy, decide the single best option for Sundarpur in subsequent year. ¡  She may also use the vaccine option only when the disease outbreak is above certain

threshold.

91 Tutorial on 27 July 2015 @ IJCAI 2015 91 Details:

Vandana Srivastava and Biplav Srivastava, Towards Timely Public Health Decisions to Tackle Seasonal Diseases With Open Government Data , International Workshop on the World Wide Web and Public Health Intelligence (W3PHI-2014), AAAI 2014

New Data Practices

�  Find correlation among methods (positive or negative) ¡  We assumed independence ¡  Needs: Historic Data, Experiment Design

�  Learn rate of return for approaches and methods (new combinations not tried in health literature) ¡  Need: Collect data on efficacy of method individually

�  Find similarity among regions ¡  Data Need: Spatio-temporal modeling/ STEM

�  Multi-objective optimization ¡  Examples: Effectiveness of approach, Reduction of case, people coverage ¡  Needs: Data about approaches tried historically


Request to Medical Community on Data

�  Report both cost and effectiveness of approaches and methods ¡  Overlooking one hampers reuse of results

�  Interact with AI community to learn and try mixed approaches that reduce cost and improve overall effectiveness ¡  All combinations cannot be tried on the ground due to practical

constraints ¡  Get more effective approaches rolled out faster targeted to new

regions


Environment Pollution

Details: Singapore (2012-2013), Varanasi (2015-)


Water Cycle (aka Hydrological Cycle)

Source: Economist, May 20, 2010 95 Tutorial on 27 July 2015 @ IJCAI 2015

Fresh Water: Supply and Demand

Source: Economist, May 20, 2010

Supply Demand


Water Challenges

�  Increasing demand due to ¡  Population ¡  Changing water-intensive lifestyle ¡  Industrial growth

�  Shrinking supplies ¡  Erratic rains due to climate change ¡  Sewage / effluent increase

�  Poor management ¡  Below cost, unsustainable, pricing ¡  Delayed or neglected maintenance

Water is the next flash point for wars


[India] Ganga – Local Ground Situation @ Varanasi (Assi/ Tulsi Ghats) + Patna

Photos of/ at Assi/ Tulsi Ghat, Varanasi on 25 March 2015 during 1700-1800 Hrs

Assi Ghat post recent cleanup Bathing on Tulsi Ghat

A nullah draining into Ganga A manual powered boat

Photos at Gandhi Ghat, Patna on 18 March 2015 during 1700-1800 Hrs


Example –River Water Pollution

�  Value – To individuals, businesses, government institutions ¡  Example – Can I take a bath? Will it cause me dysentery? ¡  Example – How should govt spend money on sewage treatment for maximum

disease reduction? �  Data – Quantitative as well as qualitative

¡  Dissolved oxygen, ¡  pH, ¡  … 30+ measurable quantities of interest

�  Access – ¡  Today, little, and that too in water technical jargon ¡  In pdf documents, website



Value of Water Pollution Data

�  Government for business decisions ¡  Source attribution ¡  Sewage treatment ¡  Public Health

�  Individuals for personal decisions ¡  Bathing (Religious, Lifestyle) ¡  Recreation ¡  Community practices


Use-case: Individual

101

�  Name: which bathing site should one use? ¡  Based on distance (cost of travel), risk of

disease, exposure to pollutants, suitability to occasion

�  Total sites in Varanasi (ghats): 87 ¡  Popular: 5 ¡  #1 religious rites (puja):

Dashashwamedh Ghat ¡  Cremation (non-bathing) ghats: 2;

Manikarnika and Harishchandra Ghat ¡  Bathing ghats: All – cremation = 85

41.  Lali Ghat 42.  Lalita Ghat 43.  Mahanirvani Ghat 44.  Mana Mandira Ghat 45.  Manasarovara Ghat 46.  Mangala Gauri Ghat 47.  Manikarnika Ghat 48.  Mehta Ghat 49.  Meer Ghat 50.  Munshi Ghat 51.  Nandesavara Ghat 52.  Narada Ghat 53.  Naya Ghat 54.  Nepali Ghat 55.  Niranjani Ghat 56.  Nishad Ghat 57.  Old Hanumanana Ghat 58.  Pancaganga Ghat 59.  Panchkota 60.  Pandey Ghat 61.  Phuta Ghat 62.  Prabhu Ghat 63.  Prahalada Ghat 64.  Prayaga Ghat 65.  Raj Ghat built by Peshwa Amrutrao 66.  Raja Ghat / Lord Duffrin bridge /

Malaviya Bridge 67.  Raja Gwalior Ghat 68.  Rajendra Prasad Ghat 69.  Ram Ghat 70.  Rana Mahala Ghat 71.  Rewan Ghat 72.  Sakka Ghat 73.  Sankatha Ghat 74.  Sarvesvara Ghat 75.  Scindia Ghat 76.  Shivala Ghat 77.  Shitala Ghat 78.  Sitala Ghat 79.  Somesvara Ghat 80.  Telianala Ghat 81.  Trilochana Ghat 82.  Tripura Bhairavi Ghat 83.  Tulsi Ghat 84.  Vaccharaja Ghat 85.  Venimadhava Ghat 86.  Vijayanagaram Ghat 87.  Samne Ghat

1.  Mata Anandamai Ghat 2.  Assi Ghat 3.  Ahilya Ghat 4.  Adi Keshava Ghat 5.  Ahilyabai Ghat 6.  Badri Nayarana Ghat 7.  Bajirao Ghat 8.  Bauli /Umaraogiri / Amroha Ghat 9.  Bhadaini Ghat 10.  Bhonsale Ghat 11.  Brahma Ghat 12.  Bundi Parakota Ghat 13.  Chaowki Ghat 14.  Chausatthi Ghat 15.  Cheta Singh Ghat 16.  Dandi Ghat 17.  Darabhanga Ghat 18.  Dashashwamedh Ghat 19.  Digpatia Ghat 20.  Durga Ghat 21.  Ganga Mahal Ghat (I) 22.  Ganga Mahal Ghat (II) 23.  Gaay Ghat 24.  Gauri Shankar Ghat 25.  Genesha Ghat 26.  Gola Ghat 27.  Gularia Ghat 28.  Hanuman Ghat 29.  Hanumanagardhi Ghat 30.  Harish Chandra Ghat 31.  Jain Ghat 32.  Jalasayi Ghat 33.  Janaki Ghat 34.  Jatara Ghat 35.  Karnataka State Ghat 36.  Kedar Ghat 37.  Khirkia Ghat 38.  Shri Guru Ravidass Ghat[5] 39.  Khori Ghat 40.  Lala Ghat

Source: http://en.wikipedia.org/wiki/Ghats_in_Varanasi

Note: ghats are specialities of most cities along Ganga – Haridwar, Allahabad, Patna


Pollu7on Example: Leather Tanneries in Kanpur, India

•  > 700 tanneries in Kanpur –  Employing > 100,000 people –  Bringing > USD 1B revenue

•  Discharge water after leather processing to river or Sewage treatment plants (STPs) –  Requirement

•  Must have their own treatment facility •  Or, have at least chrome recovery unit

–  But don’t due to costs which is a burden to main operations •  Installation •  Operations : electricity, manpower, technology upgrade, …

–  State pollution board is supposed to do inspections but doesn’t do effectively •  Government’s STPs do not process chrome, the main pollutant •  98 tanneries banned in Feb 2015 by National Green Tribunal; more

threatened


India/Ganga – Very Little Data Data.gov.in https://data.gov.in/catalog/water-quality-data-river-ganga

Sr. No. Sta,on-‐Loca,on Distance in Kms.

Dissolved Oxygen during 1986 (mg/l)

Biological Oxygen Demand in 1986 (mg/l)

Dissolved Oxygen during 2011 (mg/l)

Biological Oxygen demand during 2011 (mg/l)

1 Rishikesh 0 8.1 1.7 7.6 1.4

2 Hardwar D/s 30 8.1 1.8 7.4 1.6

3 Garhmukteshwar 175 7.8 2.2 7.5 1.7

4 Kannauj U/S 430 7.2 5.5 7.9 1.7 6 Kanpur U/S 530 7.2 7.2 7.7 3.3 7 Kanpur D/S 548 6.7 8.6 7.6 3.8

8 Allahabad U/S 733 6.4 11.4 7.8 5.3

9 Allahabad D/S 743 6.6 15.5 7.8 5.1

10 Varanasi U/S 908 5.6 10.1 8 2.9

11 Varanasi D/S 916 5.9 10.6 8 4.3 12 Patna U/S 1188 8.4 2 7 1.8 13 Patna D/S 1198 8.1 2.2 7.1 2.5


Creek Watch – Crowd Sourced Water Information Collection

As on 14 Oct 2014


Location: http://creekwatch.researchlabs.ibm.com/call_table.php

~3120 data points in 4 years from around the world

As on 14 Oct 2014


Analytics: Potential use cases S. No.

Stakeholder

Use case Data Analytical techniques

1 IT Identifying and removing outliers, data validation

Sensor data Data mining (outlier detection)

2 Individual Which bathing site to use? Sensor data, ghat data

Rule-based decision support

3 Individual/ Economy

What crops can I grow that will flourish in available water?

Sensor data, crop data

Distributed data integration, co-relation

4 Institution Determine trends/anomalies in pollution levels

Sensor data, weather data

Time series analysis, anomaly detection

5 Institution Attribute source of pollution at a location

Sensor data, demographics, industry data

Physical modeling, inversion

6 Institution Sewage treatment strategy and operational planning

Sensor data, demographics data, STP data

Multi-objective optimization

7 Institution Promoting wildlife/ dolphins Sensor data, wildlife data

Rule-based decision support


Air Pollution Analytical Models – A Birds Eye View

107

w

w

w

w

w

w

w

w

w

Meteorological Data

Hills High rise buildings

Forest

Water storage

Canyons

Sources of Air Pollutants Topography

Polluting gas emission

Industrial parks Metal firms

Motor Vehicle pollution

Industrial Wastes

Forest fires

wind

smog

rain

Temperature Humidity

Air Pollution Dispersion Analytical Models

What is the air pollution level at X (e.g., Jurong)?

Singapore


Background

�  Environmental issues such as Air Pollution and Quality (APQ) are a prominent concern for citizens and cities.

�  To monitor them and take timely action, environmental engineers collect selected data from field sensors at a limited number of locations, extrapolate them for uncovered regions.

�  The algorithms to extrapolate and analyze data are also known as analytical models (AMs).

�  An AM may be appropriate under very specific conditions - terrain type of the region, specific weather conditions, specific classes of pollutants, types of pollutant sources, data sampling rate,etc.


Issues Faced by Environmentalists

109

Identifying the right model based on the Contextual information of pollutant sources, met data and topography information

CBP

BLP

Aurora

CALPUFF CTSCREEN

ADSM CTDM

AERMOD CALINE4

HIWAY2

CAR-FMI

AEROPOL

GRAL

GATOR

OSPM

STAR-CD

ARIA-Local

PBM

TAPM

SCREEN3

SPRAY

AERSCREEN

What is the air pollution level in Jurong?

Missing Data/ NA for SpeciHic ource/region/time (precision)

CALPUFF

CALMET Data

Volume Source Data

User Specified

Deposition Velocities

User Specified Chemical

conversion rates Complex Terrain

Receptor Data File

MET Data

Identifying the Requirements of Execution platform

Execution

Platform

Data Controllers

And formatters

Executables

CALPuff Executable

Data from raw sources

Data Access through CDOM


110

Before and After

As-Is State Future State

Obtaining instance data

Collected from non-integrated infrastructure

Collected with integrated infrastructure

Discovering models Manual Automatic recommendation based on context (location and time)

Executing models with available data

Manual In-context invocation for select (supported) models; Manual for the rest

Note: This is a common problem in e-science . Other use-cases world-wide are in bioinformatics and geology .


The Solution

111

1 2 3

1 Extract entities and relations from document of these AM’s

2 Use the Domain models [a semantic model] to these AM’s to produce Semantic models of the AM’s

3 Integrate with the Discovery system using the association definitions


Kalapriya Kannan, Biplav Srivastava, Rosario U.-Sosa, Robert J. Schloss, and Xiao Liu, SemEnAl: Using Semantics for Accelerating Environmental Analytical Model Discovery, Big Data Analytics (BDA 2014), New Delhi, India, Dec 20-23, 2014.

Sample: Semantic Model – Air Pollution Concepts

112

Key Concepts: Pollutants, Pollutant Sources, Effects and Indicator.

Key Concepts Deep

Taxonomical Characterization

Reused from Existing

Scribe base


Smarter Transportation

Details: Boston (2012), New York, (2014), India – Delhi, Bangalore (2011-2015)


Press on the IBM SCC Boston team work: 1. Boston Globe, June 29, 2012 http://www.boston.com/business/technology/articles/2012/06/29/ibm_gives_advice_on_how_to_fix_boston_traffic__first_get_an_app/ (Alternative: http://bostonglobe.com/business/2012/06/28/ibm-gives-advice-how-fix-boston-traffic-first-get-app/goxK84cWB9utHQogpsbd1N/story.html) 2. Popular Science, 2 July 2012 http://www.popsci.com/technology/article/2012-07/bostons-ibm-built-traffic-app-merges-multiple-data-streams-predict-ease-congestion 3. Others: National Public Radio (USA), and a range of local TV stations on the work.

SCC Boston team with Mayor on June 27, 2012

Team at work – Source: Boston Globe article


Boston Transporta7on : Before State

GPS

Manual

Regional

Video

Road Sensors

Lots of Instrumenta7on… Not enough interconnec7on… Unexploited Intelligence…

Much Data Isolated in Silos

Mul7ple Disconnected Camera Networks

Inaccessible Data

Manual Opera7ons

Insufficient Data

"   Boston is forward-‐ thinking & progressive "   Boston recognizes climate & traffic goals are interconnected Boston is na)onally recognized for innova)on


Ecosystem Roadmap

Ci,zens

Sharing Analyzing Forward Thinking Consumer Value

Unlocking

Smarter Transportation Ecosystem

Industry

Academics

Government

Induc,ve Loop Data

Applications

Platform

Data

Ideas

Pneuma,c Tube Data

Manual Count Data

Automated Data Transfer

Online Access to Aggregated Data

Privacy Considera,ons

Ci,zen Online Access

Smarter Traffic Infrastructure

Environmental Es,mates

Mul,ple Visualiza,ons

City Benchmarks

Exploit Video Camera

Advanced Visualiza,ons

Exploit More Data Sources

Advanced Analy,cs

Deliverables "   Running Prototype "   Recommenda7ons


Common Model

Standards Aligned, Uniform format, Uniform Error Semantics

Mapping to Source

Data Transformation

Data Source Metadata

A Snapshot of Common Model and Mapping to Data Sources

Source Models


Result 1: Publicly Available Data for Mul7ple Consumers

"   Many data sources, various loca7ons & 7mes "   Stakeholders can access data easily & intui7vely

"   Locate available data sources "   Zoom in to areas of interest "   Obtain data "   Drill down to traffic pa\erns "   Assess environmental factors "   See what happens in real 7me

Researchers

Prac77oners

Planners

Engineers

Residents


•  Assign different traffic light pa\erns for different streets, 7mes •  Schedule public works projects to minimize traffic impact •  Detect changes in traffic pa\erns to drive policy changes (parking, lanes, street) •  Assess traffic impact of new landmarks •  Inform businesses, developers

Result 2: Street Classifica7on Based on Traffic Volume

Commuting

Going Home

Anomaly

Early-Bird

Night Owl Busy


Result 3: Birds-‐Eye View of City Traffic from Aggregated Data


New York: All Taxi Rides

taxi.imagework.com NYC taxi trips originate at various NY airport terminals (JFK and LGA) over the holiday season (Nov 15th to Dec 31st). Data Source: NYC Taxi & Limousine Commission Taxi Trip & Fare Data 2013 Stats 173.2M Rows | 28.85GB Tools Hadoop | Mapbox | Leaflet | jQuery | d3 | polyline | MapQuest Open Directions API

http://taxi.imagework.com/


New York: Single Taxi Ride

http://nyctaxi.herokuapp.com/


Top Cities Tourists Visit (by money spent)

Figure Courtesy: MasterCard 2014 Global Destination Cities Index, At http://newsroom.mastercard.com/digital-press-kits/mastercard-global-destination-cities-index-2014/

Top cities are getting money from tourists that countries in Middle East/ Africa / Latin America are planning by 2020


Journey Planning with Open Data


Promoting Public Transportation: Before and After We Seek

Many cities around the world, and especially in India and emerging ones, are getting their transportation infrastructure in shape.

–  They have multiple, fragmented, transportation agencies in a region (e.g., city) –  They do not have instrumentation on their vehicles, like GPS, to know about their

operations in real-time –  Schedule of public transportation is widely available in semi-structured form. They

are also beginning to invest in new, novel, sensing technologies –  Cities give SMS-based alerts about events on the road. Our approach seeks to accelerate time-to-value for such cities.

Kind of Information Today Available to Bus User

With IRL-Transit+ Benefit

Bus Schedule (static) Available online and pamphlets

Available from IT-enabled devices( low-cost phones, smart phones, web)

Increase accessibility

Bus Schedule Changes (dynamic)

No information Infer from city updates Increase information

Analytics (Bus Selection Decision Support)

No information Will be available (Transit)

Increase information

Standardization of information

No support Will be supported (SCRIBE, Transit)

Increase information’s interoperability


A Quick Review of Related Work ¡  Bay Area, USA has : http://511.org

÷  Multi-agency public authorities consortium, has advanced instrumentation ÷  It is the model to replicate

§  Google has state-of-the-art from any non-public organization. It has separate services ¡  Maps for driving guidance ¡  Transit for public transport, more than 1 mode ¡  Gaps:

÷  Considers only time, not other factors like frequency, fare and waiting time ÷  Does not integrate across their services for different mode categories ÷  Does not publish their data

¡  Acknowledgement: We use their GTFS format to consolidate schedule data

§  Many experimental systems with capabilities less than Google, ¡  DMumbai: Go4Mumbai (portal)- A http://www.go4mumbai.com/ ¡  Delhi: Disha on DIMTS (local agency) website by IIT-D, Mumbai Navigator by IIT-B; links no longer work

§  Shortest route finding algorithms from mapping companies


Journey Planning Problem �  Invariant Inputs:

¡  The person ÷  has a vehicle (e.g., car), and ÷  can also walk short distances

¡  The city has taxis, buses, metros, autos, rickshaws ÷  Buses and metros have published routes, frequency and stops ÷  Autos and rickshaws can be available at stands, or opportunistically, on the road ÷  Taxis can be ordered over the phone

�  Input: ¡  A person wants to travel from place A to B

�  Output ¡  Suggest which mode or combination of modes to select

�  Observation: Using preferences over factors that matter to users to keep commuting convenient, while making best use of available public and para-transit commute methods


Background: Public Transportation Schedule Information

�  Is widely available for public transportation agencies around the world

�  Gives the basic, static, information about transportation service

�  Usually in semi-structured format with varying semantics

�  Can have errors, missing data

Delhi Bus and Metro Data


Multi-Mode Commuting Recommender in Delhi And Bangalore

Highlights •  Published data of multiple authorities used; repeatable process • Multiple modes searched •  Preference over modes, time, hops and number of choices supported; more extensions, like fare possible •  Integration of results with map as future work; already done as part of other projects, viz. SCRIBE-STAT


Solution Steps �  Use the widely available schedule information from individual operators

(agencies) �  Clean and consolidate it across agencies and modes to get a multi-modal

view for the region ¡  Optionally: Convert it into a standard form ¡  Optionally: Enhance (fuse) it with any real-time updates about services

for the region �  Perform what-if analysis on consolidated data

¡  Path finding using Djikstra’s algorithm ¡  Analyses can be pre-determined, analyses can also be user-created

and defined �  Make analysis results available as a service

¡  On any device ¡  To any subscriber


Handling Dynamic Updates �  Invariant Inputs:

¡  The person ÷  has a vehicle (e.g., car), and ÷  can also walk short distances

¡  The city has taxis, buses, metros, autos, rickshaws ÷  Buses and metros have published routes, frequency and stops ÷  Autos and rickshaws can be available at stands, or opportunistically, on the road ÷  Taxis can be ordered over the phone

�  Input: ¡  A person wants to travel from place A to B ¡  [Optional] City provides updates on ongoing events, some may affect

traffic �  Output

¡  Suggest which mode or combination of modes to select

�  Observation: Using preferences over factors that matter to users to keep commuting convenient, while making best use of available public and para-transit commute methods

City Notifications as a Data Source for Traffic Management, Pramod Anantharam, Biplav Srivastava, in 20th ITS World Congress 2013, Tokyo


Number of SMS messages for bus stops in Delhi for 2 years (Aug 2010 – Aug 2012)*

•  344 stops with updates •  3931 total stops

* using Exact Matching


IRL – Transit in Aug 2012

Key Points • SMS message from city •  Event and location identified •  Impact assessed •  Impact used in search


Increase Accessibility and Availability of Bus Information to Passengers

Kind of Information

Today Available to Bus Users

With Solution over Phone

Mysore ITS (for reference)*

Benefit

Bus Schedule (static) Available online and pamphlets

Available from low-cost phones (Spoken Web – Static)

Available online and pamphlets

Increase accessibility

Bus Schedule Changes (dynamic)

No information today

Will be available (Spoken Web - Human)

No information but in plan


Bus Location No information today

Will be available (GPS)

Will be available (GPS)


Bus Condition No information today




Analytics (Bus Selection Decision Support)


Will be available (Transit)

No information but in plan


Last –mile Connectivity to/ from nearest stop



No information today Increase information

Standardization of information

No support Will be supported (SCRIBE, Transit)

Some support due to GPS

Increase information’s interoperability

* Opinion based on only public information; Accurate as of Jan 2014. Spoken Web is an Interactive IVR technology. SCRIBE is a ontology models for city events.


A Flexible Journey Plan Pushing the Boundaries: Information to Commuters to Reach Destination in All Eventuality

Pilots running in Dublin, Ireland

Tutorial on 27 July 2015 @ IJCAI 2015 135 Docit: An Integrated System for Risk-Averse Multi-Modal Journey Advising, Adi Botea, Michele Berlingerio, Stefano Braghin Eric Bouillet, Francesco Calabrese, Bei Chen Yiannis Gkoufas, Rahul Nair, Tim Nonner, Marco Laumanns, IBM Technical Report, 2014

•  Traffic simulation is a promising tool to do what-if analysis impacting traffic demand, supply or every-day business decisions •  What is the congestion if everyone takes out their vehicles? •  What is the impact if buses daily failure rate doubles? •  What happens if visitors constituting 20% of city traffic come for an event?

•  However, simulators need to be setup with realistic road network, traffic patterns and decision choices

•  Open data is an important source for •  Road network (e.g., Open Street Maps) •  Creating pattern (e.g., vehicle

Origin-Destination pairs, accidents) •  Framing and interpreting decision choices

Using Open Data with Traffic Simulation


New Delhi Area Selection

Area selected from openstreetmap.org with (top)(bottom)(left)(right) co-ordinates as (28.6022)(28.5707)(77.1990)(77.2522) for our experiment.


Office Timing Change Decision Choices

Tutorial on 27 July 2015 @ IJCAI 2015 138 Last second of morning commute by different strategies

Traffic References

�  Tutorial on AI-Driven Analytics In Traffic Management, in conjunction with International Joint Conference on Artificial Intelligence (IJCAI-13), Biplav Srivastava, Akshat Kumar, at Beijing, China, Aug 3-5, 2013 (tutorial-slides).

�  Tutorial on Traffic Management and AI, in conjunction with 26th Conference of Association for Advancement of Artificial Intelligence (AAAI-12), Biplav Srivastava, Anand Ranganathan, at Toronto, Canada, July 22-26, 2012 (tutorial-slides).

�  Making Public Transportation Schedule Information Consumable for Improved Decision Making, Raj Gupta, Biplav Srivastava, Srikanth Tamilselvam, In 15th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, USA, Sep 16-19, 2012.

�  Mythologies, Metros & Future Urban Transport , by Prof. Dinesh Mohan, TRIPP, 2008 �  A new look at the traffic management problem and where to start, by Biplav Srivastava, In 18th ITS

Congress, Orlando, USA, Oct 16-20, 2011. �  Arnott, Richard and K.A. Small, 1994, “The Economics of Traffic Congestion,” American Scientist, Vol.

82, No. 5, pp. 446-455. �  Chengri Ding and Shunfeng Song , Paradoxes of Traffic Flow and Congestion Pricing,


Smarter Tourism

Details: Europe (2014), India (2014-) https://sites.google.com/site/biplavsrivastava/research-1/intelligent-systems/smart-tourism


Why Tourism Matters

�  Pros ¡  Promotes services jobs ¡  Helps upgrade infrastructure ¡  Gives alternative revenue source to government beyond

traditional agriculture and manufacturing ¡  Helps take local culture world-wide ¡  Promotes country image

�  Cons ¡  Can lead to environmental impact if not planned well ¡  Can dilute local traditions and culture if unplanned


World Tourism in Numbers

Key Points •  In 2013, >1 billion people spent overnight in

another city and spent > 1 trillion USD •  France has highest visitors, USA gets the most

money and Chinese spend the most •  Among oldest civilizations (> 5K years) in the world,

of China, Egypt and India, only China gets and sends tourists in top-5 by numbers and money spent.

•  Tourists go beyond language and history to spend their money for novel experiences

Tables Courtesy: http://en.wikipedia.org/wiki/World_Tourism_rankings (Accessed 20 Oct, 2014)


Possible Strategy to Promote Tourism

�  Increase quality of experience for USPs using better information availability. Examples: ¡  Increase Service quality – Information on what is happening

and what to expect, when, at what cost; make it easy to consume offerings

¡  Remove barriers to travel and spending - Remove perception of lack-of-safety, increase transparency about supporting services like roads, hospitals, taxis

�  Promote domestic tourism in addition to international tourism ¡  Helps natives inculcate service-industry culture, build capacity


City Concierge (CC): Serving People by Design

�  Target users ¡  Citizens wanting to know more about their city ¡  Travellers planning to visit new cities with memorable experiences ¡  People (e.g., business, government) wanting to compare cities

�  Group information along a small set of easy-to-follow categories ¡  We selected - Traffic, health, building, city image, others ¡  Easy to change to any set of categories

�  Languages supported – English, Portuguese, Spanish, German ¡  Easy to extend to any

2nd place winner in Europe’s CitySDK App Hackathon in June 2014 Details: http://www.slideshare.net/biplavsrivastava/city-concierge-presentation10june2014


Serving People by Design �  Target users: Citizens, Travellers, People

Citizens, Travellers Most events – Helsinki Most open service requests - Lisbon


Check Services of Your Favorite City – Chicago, in example

Lisbon (in Portuguese) Bonn(in German)

People, Travellers Most city services – Lisbon; Traffic most common category in cities


CC Design Principles

�  Focus on features that promote usage of city data ¡  Overcoming language barriers ¡  Overcoming API and data diversity barriers ¡  Highlight commonalities, promote comparison

�  Follow standards ¡  CitySDK for tourism events upcoming ¡  Open 311 for city’s non-emergency services and service requests

�  Programming level approach ¡  Overcome (City API) errors to stay useful ¡  Be resource efficient to promote mobile apps ¡  Standardize on output formats


Prototype: Bharat Khoj – Searching Events on Mobile and Web


Research Challenges

�  ML Problems ¡  Event attendance prediction ¡  Event recommendation

�  Apply and innovate on analytics (AI) ¡  Handle data ambiguity ¡  Build reusable models

�  Focus on value (services science, AI) ¡  What metrics are being improved? Who are the agents and their

incentives? ¡  What processes will be impacted? How to boost adoption?

�  Build usable systems (software engineering, HCI) ¡  Bug-free, low-footprint, Apps ¡  Test human-comupter interfaces

�  Use (government) open data and publish output it too, preferably in semantically enriched form (data integration, AI)


Cross Domain City Comparison: Exploring a Pair of Cities

http://city-explorer.mybluemix.net/


City Comparison Functions

Example:


Exploring All Cities with Comparable Data


Best

Discussion


Smart City Challenges


�  From resource angle, decrease waste/ inefficiency while improving service delivery to citizens

�  Problems are old but accentuated today by population growth and reducing resources

�  Open Data, Effectiveness of AI Methods hold promise

�  Challenges ¡  Provide value quickly ¡  Use value synergies from different domains (e.g., health,

environment, traffic, corruption …) ¡  Grow to scale

154

Common (Descriptive) Analytics Patterns with Open Data


�  Correlation of outcomes across ¡  Data sources in same domain ¡  Different domains

�  Return of investment analysis ¡  Money invested v/s Metrics to measure improvement in

domain ¡  Comparison of performance with history ¡  Comparison of performance with other regions

155

Helping Publish Good Quality Open Data is Key


�  Have data policy in place �  Publish with best practices, have semantics, promote reuse

Figure courtesy: http://www.w3.org/TR/2015/WD-dwbp-20150625/

156

Building Community for Innovations


�  Multi-disciplinary ¡  In AI ¡  In Computer Science ¡  In science: domain (health, transport, …), techniques (CS, engg.) and

evaluation (public policy, …) �  Multi-stakeholder

¡  Citizens ¡  Government ¡  Academia ¡  Business/ Industry ¡  Non-profits, …

�  Getting to scale is key

157

Employing All Data – Data Fusion


�  Open Data is one source ¡  Often easiest to get but with issues (e.g., at aggregate level, with gaps,

imprecise semantics)

�  Social is another promising data ¡  People are anyway generating it (People-as-sensors) ¡  However, social sites have varying data reuse permissions,

license costs, access limits ¡  Big data techniques already being used here

�  Use sensor data if available ¡  Internet of Things (IoT) and big data techniques are relevant ¡  Most prevalent in health, environment and transportation

�  Key is to release the fused data also for reuse

158

Building a Technical Environment Problem Solving Community


Thank You

Merci Grazie

Gracias Obrigado

Danke

Japanese

French

Russian

German Italian

Spanish

Portuguese

Arabic

Traditional Chinese

Simplified Chinese

Hindi

Romanian

Korean

Multumesc

Turkish

Teşekkür ederim

English

Dr. Biplav Srivastava, [email protected]://www.research.ibm.com/people/b/biplav/


ai for smart city innovations with open data (tutorial)

Technology

e r s e n

o r r e s e

tutorial objectives

s t e r i n v e n t

r c h e r

r c h i n d

tutorial traffic management

available open data