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
1Tutorial 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.
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
¡  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?
¡  Volume – traffic count
¡  Speed on road
¡  City events
¡  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
CompetitiveAdvantage
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
¡  WEKATutorial:
÷  Machine Learning withWEKA: A presentation demonstrating all graphical user interfaces (GUI) in
Weka.
÷  A presentation which explains how to useWeka for exploratory data mining.
¡  WEKA Data Mining Book:
÷  Ian H.Witten and Eibe Frank, Data Mining: Practical Machine LearningTools and
Techniques (Second Edition)
÷  http://www.cs.waikato.ac.nz/ml/weka/book.html
¡  WEKAWiki: http://weka.sourceforge.net/wiki/index.php/Main_Page
—  Jiawei Han and Micheline Kamber, Data Mining: Concepts andTechniques, 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 TransportSecurity Planning Food . . . Science Health ICT
City
Responsibility
Department
Responsibility
Project
Responsibility
Task
Responsibility
Typically lacking holistic view
OperationalSystems
Before

DoIT
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
OperationalSystems
C i t y M a n a g e m e n t
Analytics, Insight, Visualization, Control Center, etc.
Water Energy TransportSecurity Planning Food . . . Science Health . . .
DoW
DoE
DoT
DoS
DoP
DoF
Do...
DoS
DoH
...
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
Uniquevaluerealized
Use of Smarter Cities capabilities
Manage 
Data1
Analyze 
Patterns2
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
26

City Level – Chicago, USA
Tutorial on 27 July 2015 @ IJCAI 2015 27As on 16 June 2015

Data.gov.in (India)
As on 16 June 2015
28

City Level – Buenos Aires, AR
As on 21 July 2015

Peek into the Future - Amsterdam
http://citydashboard.waag.org/

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…
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
41Tutorial 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 developers@cityofchicago.org 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",
"description":"Is the pole wooden or metal?",
"values":[{"key":"METAL","name":"Metal"},
{"key":"WOODEN","name":"Wooden"}]},
{"variable":true,"code":"ISTHELI3",
"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
+
Social 
Analytics
+ +
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
Paern

¡ What
is
it?:
A
paern
is
any
applica7on
using
APIs,
with
some
informa7on

generalized
(i.e.,
removed
and
parameterized)

¡ Business
Value:
A
paern

÷ 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
paerns
should
one
build
?
Theore7cally,
there
exists
a
trivial
method
to

blindly
generate
a
paern
from
any
applica7on.
Any
paern
development

process
has
to
do
beer
than
this
baseline.

÷ How
should
the
paerns
be
used
in
prac7ce?

÷ Building
a
tool-‐enabled
process
around
Paern-‐based
programming


Applica7on
Paern

¡ Approach
followed
in
AllGov

÷  Common
steps
taken
by
a
role
player
is
a
candidate
paern

÷  Common
steps
that
can
be
executed
in
the
same
infrastructure
is
a
candidate
paern

¡  Paern
1:
Browse
city
services
paern
[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,
...)

¡  Paern
2:
Create
service
request
paern
[User
Role:
Developer;
Environment:
TEST
system]

÷  Browse
city
services

÷  Browse
raised
city
service
requests

÷  Create
a
new
service
request

¡  Paern
3:
Create
service
request
paern
[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
CitizenRegistration
Officer
Satisfie
d
Not
satisfied
Vetting
Committee
Ch.Asst.Officer
NRBOfficer

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
Citizen
FrontDesk
Officer
Satisfied
Not
satisfied
Inspector
Regional
Transport
Officer
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?
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.
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, 201095Tutorial 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
¡  Example – Can I take a bath? Will it cause me dysentery?
¡  Example – How should govt spend money on sewage treatment for maximum
disease reduction?
¡  Dissolved oxygen,
¡  pH,
¡  … 30+ measurable quantities of interest
¡  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.
Stakehol
der
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
HillsHigh 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
TemperatureHumidity
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
paerns

"  

Assess
environmental
factors

"  

See
what
happens
in
real
7me

Researchers

Prac77oners

Planners

Engineers

Residents


• 
Assign
different
traffic

light
paerns
for

different
streets,
7mes

• 
Schedule
public
works

projects
to
minimize

traffic
impact

• 
Detect
changes
in

traffic
paerns
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
Increase
information
Bus Location No information
today
Will be available
(GPS)
Will be available
(GPS)
Increase
information
Bus Condition No information
today
Will be available
(Spoken Web -
Human)
No information today Increase
information
Analytics (Bus
Selection Decision
Support)
No information
today
Will be available
(Transit)
No information but in
plan
Increase
information
Last –mile Connectivity
to/ from nearest stop
No information
today
Will be available
(Spoken Web -
Human)
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

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
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
145

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
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Italian
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Portuguese
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Traditional Chinese
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Turkish
Teşekkür ederim
English
Dr. Biplav Srivastava,
sbiplav@in.ibm.com
http://www.research.ibm.com/people/b/biplav/

AI for Smart City Innovations with Open Data (tutorial)

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