AAdhar card database creation

1. Creating A Unique Identity For Every Resident
Under The Guidance
Dr. T. Nambirajan
Professor
Department of Management Studies
School of Management
BY
GROUP 8
BASIL JOHN
PANCHAMI
SARITHA
GIRIDHARAN
KABILAN
JOEL JOSEPH

2. 2
• Collection of interrelated data
• Set of programs to access the data
• DBMS contains information about a
particular enterprise
• DBMS provides an environment that it both
convenient and efficient to use.
•Database management systems were developed to
handle the following difficulties of typical file-
processing systems supported by conventional
operating systems

3. 3
The Unique ID initiative

4. 4
Principles of Aadhaar
One-time standardized Aadhaar enrollment establishes uniqueness of resident via
‘biometric de-duplication’
• Only one Aadhaar number per eligible individual
Online Authentication is provided by UIDAI
• Demographic Data (Name, Address, DOB, Gender)
• Biometric Data (Fingerprint)
Aadhaar :Subject to online authentication is proof of ID
Aadhaar enrollment / Update =
KYC
Aadhaar No. Issued,
stored in Auth. Server
“Verification” of KYC
(Authentication)

5. 5
Features of Aadhaar
Aadhaar is a 12 digit number – No Cards
Random Number – No Intelligence
Standard Attributes – No Profiling or Application Information
All Residents including Children get Numbers
Introducer System
Partnership Model
Flexibile Authentication Interface to Partners
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Benefits of Aadhaar (I)
• No fakes
• No duplicates
• To all with special focus on the marginalised and the
excluded
• Enable connectivity among databases
• Enable consolidation
Reduces
Leakages
Provides
Identities
Breaks
Silos

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Benefits of Aadhaar (II)
• Financial inclusion
• Electronic transfer of benefits
• Security of transactions
• Access to services
• Mobility in various application
• Building up of applications
Enables
Enhances
Ensures

8. 8
Registrar On-boarding Process
1. MoU with the State Government
2. Empowered Committee and Implementation Committee
3. Nodal Department and Registrar
4. KYR+ Fields
5. Vendor Selection
6. Enrolment Plan
7. IEC Activity
8. 13th
FC Funds
9. Monitoring the enrolment process
10.ICT Infrastructure
11.Aligning UID number to Databases & Government
Programmes

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Enrolment Station

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Enrolment Station

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Demographic data fields captured during Aadhaar enrolment
Field Name Comments
Name PoI documents required
Date of Birth Approximate/ Declared/ Verified
Gender M/F/T
Address PoA documents required
Parent/Spouse/Guardian Name Optional (mandatory in case of child
below 5 years)
Introducer UID Where PoI/PoA not provided

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Capture Demographic & Biometric Data
Optional data:
• Introducer data for verification
and/or
• Data of a relative who has a UID
number or an enrolment number
• Phone no., email address
Biometric Data
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Resident’s Photograph
Resident’s
Finger Prints
Resident’s Iris

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Aadhaar Letter

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UID ecosystem –
A symbiotic network
Oil Ministry
States
LIC & Banks
Income TaxOil Cos.
LPG agencies
Branches
Field agencies
Food &
Supplies
Rural
Development
Social welfare
Ration shops
Registrar
Sub-registrar
Enrolling
agency
Continuous
monitoring and
feedback

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UID Agencies

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Enrollment Agencies
Registrars
KYR, Biometrics, KYR+
Resident
UIDAI
Banks / POSB
KYR, Biometrics
Aadhaar
Aadhaar, KYR,
Photo
Aadhaar Bank A/c
Financial Inclusion - Aadhaar enabled bank account

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Process Workflow
Preparation
Activities
Enrolment
Activities
Ongoing
Activities
Verificatio
n
procedure
s
Demographic
& biometric
data capture
Data
transfer to
CIDR
CIDR
Rejections
identified
Biometric
De-
duplication
UID
Assignmen
t
Data Updation
Authenticatio
n
Certifications
Devices
Operators
Registrar
Readiness
MoU, committees etc
Process & Technology
Alignment
Prep Enrolments
Introducers
Operators, supervisors
Letter
Printing &
Delivery
Setup Enrolment
Centers
Devices, hardware,
software, connectivity
People, admin support,
logistics etc.
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UID System

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UID Architecture

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ER Diagram Flowchart

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Enrolment Data
• 600 to 800 million UIDs in 4 years
• 1 million a day with transaction, durability guarantees
• 350+ trillion matches every day
• ~5MB per resident
• Maps to about 10-15 PB of raw data (2048-bit PKI encrypted)
• About 30 TB I/O every day
• Replication and backup across DCs of about 5+ TB of incremental
data every day
• Lifecycle updates and new enrolments will continue for ever
• Enrolment data moves from very hot to cold, needing
multi-layered storage architecture
• Additional process data
• Several million events on an average moving through async
channels (some persistent and some transient)
• Needing insert and update guarantees across data stores

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Authentication Data
• 100+ million authentications per day (10 hrs)
• Possible high variance on peak and average
• Sub second response
• Guaranteed audits
• Multi-DC architecture
• All changes needs to be propagated from enrolment data stores to
all authentication sites
• Authentication request is about 4 K
• 100 million authentications a day
• 1 billion audit records in 10 days (30+ billion a year)
• 4 TB encrypted audit logs in 10 days
• Audit write must be guaranteed

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Aadhaar Data Stores
Mongo cluster
(all enrolment records/documents
– demographics + photo)
Shard
1
Shard
4
Shard
5
Shard
2
Shard
3 Low latency indexed read (Documents per sec),
High latency random search (seconds per read)
MySQL
(all UID generated records - demographics only,
track & trace, enrolment status )
Low latency indexed read (milli-
seconds per read),
High latency random search (seconds
per read)
UID master
(sharded)
Enrolment
DB
Solr cluster
(all enrolment records/documents
– selected demographics only)
Low latency indexed read (Documents per sec),
Low latency random search (Documents per sec)
Shard
0
Shard
2
Shard
6
Shard
9
Shard
a
Shard
d
Shard
f
HDFS
(all raw packets)
Data
Node 1
Data
Node 10
Data
Node ..
High read throughput (MB per sec),
High latency read (seconds per read)
Data
Node 20
HBase
(all enrolment
biometric templates)
Region
Ser. 1
Region
Ser. 10
Region
Ser. ..
High read throughput (MB per sec),
Low-to-Medium latency read (milli-seconds per read)Region
Ser. 20
NFS
(all archived raw packets)
Moderate read throughput,
High latency read (seconds per read)
LUN 1 LUN 2 LUN 3 LUN 4

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Systems Architecture
•
Work distribution
using SEDA &
Messaging
•
Ability to scale within
JVM and across
•
Recovery through
check-pointing
•
Sync Http based Auth
gateway
•
Protocol Buffers &
XML payloads
•
Sharded clusters
•
Near Real-time data delivery to warehouse
•
Nightly data-sets used to build dashboards,
data marts and reports
•
Real-time monitoring using Events

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Enrolment Biometric Middleware
• Distribute, Reconcile biometric data extraction and
de-dup requests across multiple vendors (ABISs)
• Biometric data de-referencing/read service(Http) over
sharded HDFS and NFS
• Serves bulk of the HDFS read requests (25TB per day)
• Locate data from multiple HDFS clusters
– Sharded by read/write patterns : New, Archive, Purge
• Calculates and maintains Volume allocation, SLA
breach thresholds of ABISs
• Thresholds stored in ZK and pushed to middleware
nodes

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Event Streams & Sinks
• Event framework supporting different interaction/data
durability patterns
• P2P, Pub-Sub
• Intra-JVM and Queue destinations - Durable / Non-Durable
• Fire & Forget, Ack. after processing
• Event Sinks
• Ephemeral data consumed by counters, metrics (dashboard)
• Rolling file appenders that push data to HDFS
– Primary mechanism for delivering raw fact data from
transactional systems to the warehouse staging area

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Data Analysis
• Statistical analysis from millions of events
• View into quality of enrolments – e.g. Enrolment
Agencies, Operators
• Feature introduction – e.g. Based on avg. time taken for
biometric capture, demographic data input
• Enrolment volumes – e.g. By Registrar, Agency,
Operator etc
– Useful in fraud detection
• Goal to share anonymized data sets for use by
industry and academia – information transparency
• Various reports – Self-serve, Canned, Operational
and/or Aggregates

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UID BI Platform
Data Analysis architecture
Data Access Framework
UIDAI Systems Events
(Rabbit MQ)
Server DB
(MySQL)
Hadoop HDFS
Data Warehouse (HDFS/Hive)
Event CSV
Fact DataDimension Data
Datasets
On-Demand Datasets
Datamarts
(MySQL)
Raw Data
Dimension Data
(MySQL)
Pig
Pentaho Kettle
Hive
Pentaho Kettle
Canned Reports Dashboard
Self-service
Analytics
Pentaho BI
FusionCharts
E-mail/Portal/Others

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FIELD NAME DATA TYPE KEY
NAME VARCHAR CONSTRAINT
MARITAL
STATUS
VARCHAR CONSTRAINTS
ADDRESS VARCHAR CONSTRAINTS
PHONE
NUMBER
NUMBER CONSTRAINTS
PINCODE NUMBER CONSTRAINTS
REGISTER NO NUMBER PRIMARY KEY
PERSONAL DETAILS 1

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FIELD NAME DATATYPE KEY
NAME VARCHAR CONSTRAINTS
Y.O.B DATE CONSTRAINTS
GENDER VARCHAR CONSTRAINTS
REGISTER NO NUMBER FOREGIN KEY
PERSONAL DETAILS 2

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• Data Query language
• Retrieve
• update
• Data Manipulation Language
• update
• delete
• Data Definition Language
• Create
• insert
• Transaction Language
• Commit
• Revoke
• savepoint
FUNCTION USED IN THE DATABASE CREATION

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• CREATION:
Create table tablename(columnname datatype(size),columnname datatype(size))
Create table person details(name varchar(10),marital status varchar(12),address
varchar(20),phone number number(10),pincode number(7),register no number(17));
PROCESS OF DATABASE

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INSERTION:
Insert into tablename[(columnname,columnname)]
Values (expression,expression);
Insert into table person details(name , maritalstatus, address, phone number ,pincode ,
register number)values(“xxx”, “w/o yyy “,”no:14,nehru street kamaraj nagar
puducherry”,”9123456789”,”605011”,”3560 2513 1913”);

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• UPDATION:
• Update tablename set columnname=expression, columnname=expression…. Where
columnname=expression;
– Update personal details set name=“www”
– Where pincode=“605011”;

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• RETRIVAL
• SELECT columnname,columname from tablename;
– Select name , register no from personal details;
– Select name and register no from personal details where phone number=“9123456789”
DELETION:
DELETE FROM tablename;
DELETE from personal details;

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• AVG - avg({distinct all}n)
• MIN - min({distinct all}expr)
• COUNT(expr) - count({distinct all}expr)
• COUNT(*) - COUNT(*)
• MAX - max({distinct all}expr)
• SUM - sum({distinct all}n)
• INITCAP - INITCAP(char)
• LTRIM - LTRIM(char[,set])
ORACLE FUNCTIONS

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Challenges in India Identity Card

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References
• Aadhaar Portal :
https://portal.uidai.gov.in/uidwebportal/dashboard.do
• Data Portal :
https://data.uidai.gov.in/uiddatacatalog/dataCatalogH
ome.do
• Analytics whitepaper :
http://uidai.gov.in/images/FrontPageUpdates/uid_doc
_30012012.pdf

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