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BigData to BigWisdom
Daniel Hulme - d.hulme@cs.ucl.ac.uk
• Masters (Msci) in Computer Science with Machine Learning @ UCL
• Doctorate (EngD) in Computational Complexity @ UCL
• Research Scientist in Optimisation & Innovation @ UCL
• Co-lecturer of New Venture Analytics @ UCL
• Founder & CEO of Satalia (NPComplete Ltd) @ UCL
• Recipient of a Kauffman Global Scholarship
• Visiting Fellow in BigData @ The Big Innovation Centre
BIG WISDOM
BIG UNDERSTANDING
BIG KNOWLEDGE
BIG INFORMATION
BIG DATA
DIKUW Pyramid
• Optimisation Algorithms
• Decision Making
• Decision Science
• Machine Learning
• Analytics & Visualisation
• Data Science
• Aggregation & Visibility
• Access & Storage
• Security & Resilience
ACTION
INSIGHT
DATA
Understanding BigData
What is BigData?
Giving Meaning to Data
Structured
 Databases
 Siloed
 Migrating from
 Storage
 Corruption
 Security
 Mining
Unstructured
 Internet
 Trawling
 Mining
 Language
 Privacy
 Cleaning
 Authenticity
Semi-structured
 Semantic Web
 Tagging
 Querying
 Provenance
 Mining
 Storage
 Migrating to
Why?
Machine Learning
 Mature subject
 Complex Correlations
 Open-source Tools
 Mining
 Prediction
 Hard
Semantic Inference
 Reasoning
 New research area
 Semantic Web
 Emerging Tools
Pretty Pictures & Data Scientists
The Use of Knowledge
Wisdom - Buy John a dog bowl
for his birthday and he'll be very
happy
Understanding - John's birthday
is on April 27th. If John Smith likes
Dogs then he probably has one
Knowledge - 1979-04-27 is John
Smith's date of birth, and John
Smith likes Dogs
Information - 1979-04-27 is a
Date, John Smith is a Person, Dog
is an Animal (data in context)
Data - "19790427", "John
Smith", "Dog" (raw groups of
symbols)
BigQuestions
 What problem are you
trying to solve?
 Objectives, Variables
and Constraints
POINTS ROUTES MEGA OPS /S
10 3,628,800 4 seconds
11 39,916,800 1 minute
13 6,227,020,800 2 hours
14 87,178,291,200 1 day
16 20,922,789,888,000 1 year
20 2,432,902,008,176,640,000 77,000 years
22 1,124,000,727,777,610,000,000 36 millennia
24 620,448,401,733,239,000,000,000 20 billion years
Odd or Even: O(1)
Ordered Search: O(log n)
Sorting Items: O(n2)
Travelling Salesman: O(n!)
Knowledge, Power, Responsibility
Change the World
• Challenges
– How anonymous is anonymised data?
– What data should be open and how can it be used/abused?
– What should be BigData standards, protocols and ontologies?
• Opportunities
– What new innovations can emerge from BigData?
– What about Personalised Medicine, Health, Education?
• Pioneers
– JDI DataLab at UCL is uniquely positioned to address these
challenges and explore emerging opportunities
– Facilitate exciting interdisciplinary collaborations across JDI, CS,
CASA, BEAMS, Bartlett, Enterprise, and beyond
Questions & Discussions
BIG WISDOM
BIG UNDERSTANDING
BIG KNOWLEDGE
BIG INFORMATION
BIG DATA

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UCL DataLab Launch - BigData to BigWisdom

  • 1. BigData to BigWisdom Daniel Hulme - d.hulme@cs.ucl.ac.uk • Masters (Msci) in Computer Science with Machine Learning @ UCL • Doctorate (EngD) in Computational Complexity @ UCL • Research Scientist in Optimisation & Innovation @ UCL • Co-lecturer of New Venture Analytics @ UCL • Founder & CEO of Satalia (NPComplete Ltd) @ UCL • Recipient of a Kauffman Global Scholarship • Visiting Fellow in BigData @ The Big Innovation Centre
  • 2. BIG WISDOM BIG UNDERSTANDING BIG KNOWLEDGE BIG INFORMATION BIG DATA DIKUW Pyramid • Optimisation Algorithms • Decision Making • Decision Science • Machine Learning • Analytics & Visualisation • Data Science • Aggregation & Visibility • Access & Storage • Security & Resilience ACTION INSIGHT DATA Understanding BigData
  • 4. Giving Meaning to Data Structured  Databases  Siloed  Migrating from  Storage  Corruption  Security  Mining Unstructured  Internet  Trawling  Mining  Language  Privacy  Cleaning  Authenticity Semi-structured  Semantic Web  Tagging  Querying  Provenance  Mining  Storage  Migrating to
  • 5. Why? Machine Learning  Mature subject  Complex Correlations  Open-source Tools  Mining  Prediction  Hard Semantic Inference  Reasoning  New research area  Semantic Web  Emerging Tools
  • 6. Pretty Pictures & Data Scientists
  • 7. The Use of Knowledge Wisdom - Buy John a dog bowl for his birthday and he'll be very happy Understanding - John's birthday is on April 27th. If John Smith likes Dogs then he probably has one Knowledge - 1979-04-27 is John Smith's date of birth, and John Smith likes Dogs Information - 1979-04-27 is a Date, John Smith is a Person, Dog is an Animal (data in context) Data - "19790427", "John Smith", "Dog" (raw groups of symbols) BigQuestions  What problem are you trying to solve?  Objectives, Variables and Constraints POINTS ROUTES MEGA OPS /S 10 3,628,800 4 seconds 11 39,916,800 1 minute 13 6,227,020,800 2 hours 14 87,178,291,200 1 day 16 20,922,789,888,000 1 year 20 2,432,902,008,176,640,000 77,000 years 22 1,124,000,727,777,610,000,000 36 millennia 24 620,448,401,733,239,000,000,000 20 billion years Odd or Even: O(1) Ordered Search: O(log n) Sorting Items: O(n2) Travelling Salesman: O(n!)
  • 9. Change the World • Challenges – How anonymous is anonymised data? – What data should be open and how can it be used/abused? – What should be BigData standards, protocols and ontologies? • Opportunities – What new innovations can emerge from BigData? – What about Personalised Medicine, Health, Education? • Pioneers – JDI DataLab at UCL is uniquely positioned to address these challenges and explore emerging opportunities – Facilitate exciting interdisciplinary collaborations across JDI, CS, CASA, BEAMS, Bartlett, Enterprise, and beyond
  • 11. BIG WISDOM BIG UNDERSTANDING BIG KNOWLEDGE BIG INFORMATION BIG DATA