Big Data & Analytics for Government - Case Studies

Big Data & Analytics:
what this means to
Governments
John Palfreyman

Agenda
1. Big Data & Analytics for Government - Why?
2. Case 1 – Galway Bay Sonar
3. Case 2 – Base Protection
4. Case 3 – Predictive Policing
5. Case 4 – Ebola initiatives in Africa
6. Future - Watson
© 2014 International Business Machines Corporation

3
External Pressures on Government
Accelerated
globalization
Continuing
economic and
budget challenges
Expanding impact of
technology
Increased expectations
for services and
responsiveness
Rising
environmental
concerns
Pressure for
transparency and
accountability

4
Big Data = Huge opportunity, if harnessed
Variety Velocity
Veracity Volume
4.6 billion
camera phones world wide
Facebook processes
10 TBs of data
every day
12 terabytes of Tweets
each day, insight into public
sentiment
2 billion
people on the Web as of
2011
5 Million
financial transactions occur
every single day
5 billion
mobile phones in use

Big Data – Increasing Veracity
The Dawn of Big Data: This is Only the Beginning
The uncertainty of big data is growing alongside its complexity
9000
2010
Social
Media
© 2013 International Business Machines Corporation 42
2015
Sensors
& Devices
VoIP
Enterprise
Data
We are here
8000
7000
6000
5000
4000
3000

Analytics can transform Government
To create a strong legacy
of transformation
To spend public
funds responsibly
To drive smarter
decision-making
To realize results-based
government
To drive
transparency and
accountability
To achieve the best outcomes
for everyone, from everyone

Galway Bay Marine Mammal Project
Identify marine mammals
• Species
• Count
• Distance
• Individual returning mamals
Method
• Analysis of hydrophone data
• High frequency (500 kHz)
• Medium resolution (16bit mono)
• Contain environmental (natural and artificial) noise

Sensor Array
Transform
Filter / Sample
Classify
Correlate
Annotate
9
Stream Computing

Species Identification
• “Click Detection” and “Click Profiling”
• Three stages process
Pre-click
detection
Dynamic
filtering
Click
profiling &
detection

Pre-click Detection
High Pass
Filter
Pre-click
detector
Fast Fourier
Transform
(FFT)
Mean
Frequency
About 0.5s of WAV data
Porpoise
f=137-144kHz
Dolphin
f=115-120kHz

Dynamic Filtering
Dolphin
f=115-120kHz
12
Band Pass
Porpoise Filter (175 dB)
f=137-144kHz
Calculate
Sound
Pressure
Level
Sound pressure level (signal
strength) determined by:
• Distance
• Salinity
• Temperature
Apply filter based on:
• Species “hint” (frequency)
• Sound pressure level
Band Pass
Filter (161 dB)
Band Pass
Filter (151 dB)
Band Pass
Calculate Filter (230 dB)
Sound
Pressure
Level
Band Pass
Filter (216 dB)
Band Pass
Filter (210 dB)

Click Profiling & Detection
13
Mean
Frequency
Fast Fourier
Transform
Band Energy
Peak Position &
Width
Click Length
Click
Counter
Spectral frequency
in click

Base Protection - Project Overview
Requirement
• Detect, classify, locate and track potential threats, above and below
ground, to secure base perimeters and border areas
Challenges
• Continuously consume and analyse digital acoustic data
– biological, mechanical and environmental objects-in-motion
• Gather and analyse information simultaneously, at very high speed
Capability
• Collect data from multiple sensor types
• Analyse and classify streaming acoustic data in real time

Base Protection – Solution Outline
Fibre Optic Cable Base Perimeter
Detect
Classify
Locate
Track
Streaming, Time Series and Partner Technology

Base Protection - Capability
• Captures and transmits real-time, streaming acoustical data from
around the base
• Enables security personnel to “hear” even when the incident miles away
• Identify and classify a potential security threat
• Take appropriate action
• Capture, reduce, process and analyse 275Mbit of acoustic data from
1024 individual sensor channels in 1/14th second (42 TB/day)
• Extendable to include other sources (reduced false alarm rate)
• Airborne
• Video

FROM: Traditional Analysis & Classification
Hydrophone
Array
Beam
Forming
Bearing / Time
Detection
Classification
Tracking
Digital Signal Processing
Fast, dedicated purpose hardware / firmware
Intercept data stream
Look for patterns, trends, characteristics
History

TO: Adaptive Analysis & Classification
Hydrophone
Array
Beam
Forming
Bearing / Time
Detection
Classification
Tracking
Stream Computing
As fast, low latency
Signal Processing Functions
Adaptive
History
hadoop technologies
Offline Analysis
Build Models & Patterns
Condition Real Time
Processing

Police Case Work
Domestic Violence Reduction Unit (DVRU)
o 3,000+cases referred each year
o Investigate ~15%
Original process
o Manual review of case
o Decision by team based on experience
Challenges
o Time spent reviewing cases
(20% of overall unit; 2FTE)
o Manual decision process:
Biased? Liability? Best result?

Project Approach
• Data integration (SAS, Excel, SQL, CSV..)
• Visualization
• Development of multivariate predictive models
• Integration of standardized scoring and item
weighting
• Text analytics
• Entity analytics (clustering and linking)
• Automated scoring based on standardized
input
Understand
Goal
Understand
Data
Data
Preparation
Modelling
Data
Deploymen t Evaluation

Project Outcomes
• Fact-based decision making drives consistent and
better results
• Standardized protocol for reviewing and assigning
cases: procedural consistency
• Risk information available for prosecutors,
probation boards
• Data collection improved to provide input needed
for evaluation model
• Increased productivity
o Unit strength decreased (9 to 7 officers)
o 111% in cases investigated (453 to 954)
o 21% increase in arrest rate

Ebola Initiatives in Africa
1. Citizen engagement and analytics system in
Sierra Leone
• Communities communicate issues directly to
government
2. IBM Connections technology donation to Nigeria
• Coordinate public health efforts
3. Global platform for sharing Ebola-related data
4. ALL philanthropic

Citizen Engagement & Analytics (Sierra Leone)
• Citizen Reporting, promoted over radio
• mobile voice – toll free number
• toll free SMS number, via Airtel
• Machine learning & topic classification to
identify clusters of issues
• Heat maps using spatio temporal data
• Passed to Open Government
• cction & policy to contain disease

The Jeopardy Challenge
• Jeopardy = US TV game show
• English-‐language
ques/ons,
word
plays
• understand
complex
natural
language
• large
knowledge
base
to
find
the
best
answer
• Ability
to
“train”
from
previous
shows
• Grand challenge in automatic, open domain
question-answering
• IBM Research led
• Massive effort
• Won Jeopardy, beating champions
• But then what?

Cognitive systems
Programmatic Systems
• Leverage traditional data sources
• Follow pre-defined rules (programs)
• Provide the same output to all users
Cognitive Systems
• Are taught, not programmed.
• Learn and improve based on experience
• Interpret sensory & non-traditional data
• Relate to each of us as individuals
• Expand and scale our own thinking

Expanding Watson Post Jeopardy
Explores
Reasons
Visualizes
Understands natural
language
Generates and
evaluates hypotheses
Adapts and learns
© 2014 International Business Machines
Corporation
30

Three classes of cognitive services
ASK DECIDE
© 2014 International Business Machines
Corporation
DISCOVER
Seek answers and
insights from a defined
data repository
comprised largely of
unstructured data
Provide supporting
evidence for
confidence weighted
responses to questions
User has a question
and answer
requirement, with
questions posed in
natural language

Decision Support : Healthcare
32

Summary
Predictive analytics § Predict and target the needs of citizens and match programs
and resources to meet highest-priority citizen needs.
§ Predict and help prevent outages in key public services.
§ Match programs and resources to meet highest-priority citizen
needs.
§ Position resources to focus on high-priority service areas.
§ Improved governance, reduced risk, and compliance
Analytical decision management
§ Get a strategic view to manage the delivery of citizen services
and program requirements.
§ Position resources to focus on high-priority service areas.
Business intelligence
Business outcomes/benefits
§ Strategic view of revenue streams, budgets, costs and
expenses at all levels of the government enterprise.
§ Leverage collaborative budget preparation and execution.
Performance management
Risk management § More effectively measure and monitor financial and
operational risk across agencies.
§ Use reporting capabilities to support compliance with internal
and external requirements.

Conclusion
35
1. Big Data = huge opportunity, if harnessed
2. Quality erodes if increasing amounts of low veracity data ignored
3. Stream Computing + hadoop = Adaptive Signal Processing
4. Analytics solutions can make a REAL difference
5. Future = Cognitive underpinning of Analytics

36
Questions?
John Palfreyman
2dsegma@uk.ibm.com

Big Data & Analytics for Government - Case Studies

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Big Data & Analytics for Government - Case Studies