SMALL SENSORS. BIG DATA. FROM CLARITY TO INSIGHT IN THE WORLD OF THE SENSOR WEB Barry Smyth, INSIGHT Centre for Data Analytics @barrysmyth, [email protected] Tuesday 1 October 13
SMALL SENSORS. BIG DATA.FROM CLARITY TO INSIGHT IN THE WORLD OF THE SENSOR WEB
Barry Smyth, INSIGHT Centre for Data Analytics@barrysmyth, [email protected]
Tuesday 1 October 13
In a typical lifetime ...
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 billion breaths 2.5 billion heart beats 100 million litres oxygen 100 trillion cells 50,000 litres water 70 tonnes food 70 million calories 3 years toilet 1 billion km 1 year traffic 50,000 kms walking 0.5 million kWh 250 tonnes coal 50 tonnes waste 12 years work 500 days sick 150,000 yawns 28 years asleep 2 years reading 9 years of TV
Tuesday 1 October 13
1 = 10bytes18exabyte
Tuesday 1 October 13
1 = 10bytes18exabyte
1000,000,000,000,000,000
Tuesday 1 October 13
1 = 10bytes18exabyte
20,000 x all of the printed material in the
US Library of Congress.Or all of the words spoken by humans. Ever!
Tuesday 1 October 13
1 = 10bytes18exabyte
6 !hours
But, we now create this much information every
Tuesday 1 October 13
Connecting atoms & bits.
From algorithms to data.
Tuesday 1 October 13
A PARADIGMSHIFT
algorithmdata
algorithm
dataTuesday 1 October 13
VSIT’S NOT (JUST) ABOUT THE DATA
N = all
Messy & Diverse
Reusable
Correlation
N = small
Clean & Uniform
Disposable
Causation
Tuesday 1 October 13
computation
sensorsdev
data
Tuesday 1 October 13
THE WORLD’S FIRSTSUPERCOMPUTER
Brainchild of Seymour Cray, in 1964 the closet-sized CDC 6600 was the biggest, baddest computer of the age.
5,500 kgs, 480 kb RAM, 3M FLOPs, $60m
Tuesday 1 October 13
MOORE’S MAGICAL LAWS
In 1965, Intel co-founder, Gordon Moore, noted a doubling of “computing power” every 1-2 years and predicted that this would continue for at least 10 years...
... this became known as Moore’s Law.
Tuesday 1 October 13
Tuesday 1 October 13
A SELF-FULFILLING PROPHECY?
CDC 6600
[1964]
IBMPC[1981]
iPHONE 5S[2013]
0.05FLOPs/$
200FLOPs/$
8MFLOPs/$
x4,000 x40,000
Tuesday 1 October 13
TO PUT THIS INTO PERSPECTIVE ...
The iPhone 5S is about 60,000 times more powerful that the Apollo 11’s guidance computer.
Tuesday 1 October 13
IF MOORE’S LAW APPLIED TO CARS?
“If the auto industry had moved at the same speed ... ...your car today would cruise comfortably at a million miles an hour and probably get a half a million miles per gallon of gasoline. But it would be cheaper to throw your Rolls Royce away
Tuesday 1 October 13
RAYKURZWEIL
“A computer that once fit in a building, when I was a student, now fits in my pocket and is one thousand times more powerful despite being a million times less expensive.”
Tuesday 1 October 13
MEMORY, DISK SIZE, BANDWIDTH, PIXELS,...
All subject to Moore’s Law like improvements over the past 30 years...
... except for battery power / energy density.
Tuesday 1 October 13
Tuesday 1 October 13
THE RISE OF THESENSORWEB
Tuesday 1 October 13
UBIQUITOUS COMPUTING
Mark Weiser’s 1988 vision for a Post-PC world saw computing evolve from a terminal-based paradigm to one in which computing and computation would simply disappear into the fabric of our world.
Tabs, Pads, Boards ⇒ Smart Dust, the Internet of Things, Wearable Computing
Tuesday 1 October 13
THE EMERGINGSENSOR WEB
Tabs
Pads
Boards
Smart Sensors
The Internet of Things
Wearable Computing
Tuesday 1 October 13
A MATERIALS SCIENCE DETOUR
Chemistry & Physics ⇒ Novel Materials & Structures
Common Materials ⇒ Next Generation Sensors
Tuesday 1 October 13
NOKIA’S MORPH CONCEPT DEVICE
Tuesday 1 October 13
CHALLENGES OF PHYSICAL SENSING
Conventional sensors (thermistors, flow meters, photoreceptors).
Biofouling & Calibration.
Robustness, Reliability, Energy & Communications.
Cost, Cost, Cost.
Tuesday 1 October 13
SWEATSENSING
Microfluidic, Lab-on-a-Chip, Wearable.
pH sensitive dye &photo-detector.
Accurate, continuous, realtime
Athletic performance ⇒ Cystic Fibrosis
Tuesday 1 October 13
UNIVERSAL MOBILE SENSING PLATFORM
Tuesday 1 October 13
UNIVERSAL MOBILE SENSING PLATFORM
C A M E R A
MICROPHONES P E E D
L I G H T
O R I E N T A T I O N
HUM IDITY
T E M P E R A T U R E
LOCATION
TOUCH
MOTION
DIRECTIONF I N G E R P R I N T S
Tuesday 1 October 13
Connectivityhigh-speed data
Mobilitylocation-aware
Poweralways on
Tuesday 1 October 13
THE QUANTIFIED SELF MOVEMENT
A data-rich approach to everyday living.
Gordon Bell (Microsoft) and the My Life Bits project ⇒ Digitizing everyday life.
SenseCam
Tuesday 1 October 13
7 YEARS3 MONTHS2 WEEKS 1 PERSON12M PHOTOS1TB
Tuesday 1 October 13
Activitiesclassificationsummarisation
Lifestylebehaviourspreferences
Eventssegmentation
clustering
Tuesday 1 October 13
Tuesday 1 October 13
THE DISRUPTION OF HEALTHCARE
Always-on personal sensing, 24/7/365
The Creative Disruption of Healthcare
Activity and exercise, sleep and moods, food, blood glucose, heart rate, pulse ox, lung function, ...
Tuesday 1 October 13
THERE’SAN APPFOR THAT
Tuesday 1 October 13
EXERCISE & FITNESS
Runkeeper iPhone/Android
Running, Walking, Biking, ...
Age, gender, weight, ...
Location, pace, duration, climb, calories, heart rate,...
Tuesday 1 October 13
TRACKING SLEEP
Basic ‘sleep tracking’based on motion.
Duration vs Movement
Sleep Quality (≈ time/move)
Sleep Notes / Wakeup Moods
Comparative AnalyticsTuesday 1 October 13
MOOD & FOCUS
The Melon Headband
Uses EEG to track brain activity to assess ‘focus’.
Tagging, location, and activity information helpsusers to better assess what impacts their focus.
Tuesday 1 October 13
FOOD & NUTRITION
Meal logging and nutritionalanalysis.
Manual vs Semi-Automatic.
Calorie goals and diet plans.
Integrated weight tracking.
Tuesday 1 October 13
HEART RATE SENSING
Using smartphone camera with your finger. No external sensor required.
Detecting colour changes due to capillary blood-flow.
Tagging, comparative analytics etc
Tuesday 1 October 13
BLOOD GLUCOSE
External blood glucose sensorautomatically syncs readings with app.
Readings tagged withmealtime, exercise etc.
Analysis and visualization of trends, logs, stats.
Tuesday 1 October 13
MOBILE SPIROMETRY
Using a mobile phone microphone to evaluate lung function.
FVC, FEV, PEF measures.
Audio ⇒ Features ⇒ Machine Learning.
Mean 5.1% error wrt clinical spirometry ⇒ suitable for home-based monitoring.
Tuesday 1 October 13
MOBILE SPIROMETRY
Using a mobile phone microphone to evaluate lung function.
FVC, FEV, PEF measures.
Audio ⇒ Features ⇒ Machine Learning.
Mean 5.1% error wrt clinical spirometry ⇒ suitable for home-based monitoring.
Tuesday 1 October 13
SENSORS& SPORTS
Profs Brian Caulfield & Niall Moyna (@ CLARITY)
Player Health vs Performance Analysis
Rugby, Athletics, Cycling, Equestrian, Archery, Boxing, GAA, ...
Tuesday 1 October 13
AUTOMATIC TACKLE CLASSIFICATION
GPS +Accelerometer
Tuesday 1 October 13
CONSUMER-DRIVEN HEALTHCARE?
Towards preventative, sensor-based, data-driven healthcare.
Sparse checkups ⇒ 24/7/365 Sensing
The data is ours to share ...
Apps vs Prescriptions?
Tuesday 1 October 13
ALWAYS ONMOBILESENSING
Tuesday 1 October 13
SCALINGTuesday 1 October 13
vertical scalingTuesday 1 October 13
vertical scalingTuesday 1 October 13
horizontal scalingTuesday 1 October 13
horizontal scalingTuesday 1 October 13
PARTICIPATORYSENSINGTuesday 1 October 13
ASTHMOPOLIS SMART INHALER
Tuesday 1 October 13
PARTICIPATORY SENSING
Tuesday 1 October 13
HACKING YOUR COMMUTE
GPS & Navigation Assistants
Map Apps Rule the World
TomTom, Garmin, Google, Apple, Nokia, ...
Tuesday 1 October 13
CROWDSOURCED MAPPING (WAZE)
Free smartphone app.
Real-time sensing of users’location, time, speed etc.
x millions of users
= social mapping + traffic flow, alerts, hazards, ...
Tuesday 1 October 13
Tuesday 1 October 13
Tuesday 1 October 13
CITIZEN SENSING PUBLIC TRANSPORT
Roadify (iPhone App)
Status updates for publictransport experiences.
Train, bus, subway, ferry,parking, ...
Opinions ⇒ Alerts, Recommendations, Delays, ...
Tuesday 1 October 13
TURNING PEOPLE INTO SENSORS
Participatory/Citizen Sensing
Big, messy data ⇒ real-time insights.
The smartphone as a mobile sensor platform...
... and the willingness of people to contribute to data to causes that matter
Tuesday 1 October 13
FROM REALTO VIRTUALSENSORS
Tuesday 1 October 13
MINING THE DATA EXHAUST
From Real to Virtual Sensors
Page Views, Read Times, Mouse Movements, Search Queries, Result Clicks, Social Connections, Share, Comments, Likes, Posts, Emails, IMs, ...
Tuesday 1 October 13
THE ORIGINAL BIG DATA COMPANY
Mining relevance & reputation from links.
Search logs as sensor data.
Tuesday 1 October 13
PAGERANK GOOGLE’S BIG IDEA
The importance of a page as a ranking signal.
Estimating importance from in-links ...
... and PageRank was clever way to count in-links to
accurate estimate importance.
Tuesday 1 October 13
GOOGLE’S BIGGER IDEA
$40 billion
Google’s real Bigger Idea was that it’s search engine could sense our intentions through our queries and click ...
... and that it could match this demand with real-time supply through its search adverts.
Tuesday 1 October 13
SEARCH LOGS AS SENSOR DATA
“... Web search ... can be likened to a large-scale distributed network of sensors for identifying potential side effects of drugs. There is a potential public health benefit in listening to such signals, and integrating them with other sources of information.”
“Web-Scale Pharmacovigilance: Listening to Signals from the Crowd” J Am Med Inform Assoc. (2013)
Tuesday 1 October 13
SENSING DRUG SIDE-EFFECTS
82MQueries
6MUsers
Tuesday 1 October 13
SENSING FLU TRENDS
Identified trigger terms correlated we known past outbreaks. Tracked real-time occurrence of these terms, location by location to deliver accurate* regional outbreak maps that correlated well with verifiedCDC data.
Tuesday 1 October 13
TURNING BROWSERS INTO BUYERS
Understanding user preferences.
Making personalized suggestions.
Tuesday 1 October 13
itemsus
ers
Tuesday 1 October 13
itemsus
ers
Correlations between the ratingspatterns of users denote user similarity ...
People like you have also liked ...
Tuesday 1 October 13
itemsus
ers
Conversely correlations between the ratings patterns of items denote item similarity ...
If you liked X then you might like Y...
Tuesday 1 October 13
MINING USER-GENERATED REVIEWS
Tuesday 1 October 13
USER-GENERATED REVIEWS
+‘vesstaff
locationbed
servicebreakfast
-‘vesnoiseelevatorscarpethealth clubpublic transport
Chicago Hotels
Tuesday 1 October 13
OPINION AMPLIFICATION
Twitter, FaceBook as a source ofreal-time opinions.
Raw Text ⇒ Sentiment ⇒ Opinion
These days Twitter data has been used to predict election outcomes, box office success, and musical talent ...
Tuesday 1 October 13
Participatory sensing as collective intelligence
Human Intelligence + Brute-Force Computation
TOWARDS COLLECTIVE INTELLIGENCE
Tuesday 1 October 13
DEALING WITH EMAIL SPAM
Back in 2000 Yahoo had a problem ...
Bots registering free email accounts for the purpose of bulk spam.
How to recognise real people from the spambots?
Luis Von Ahn
Manuel Blum
Tuesday 1 October 13
Yahoo! Mail CAPTCHATuesday 1 October 13
250mCAPTCHASPER DAY
150kPERSON-HOURSPER DAY
7mPERSONHOURS
45CAPTCHADAYS!
Tuesday 1 October 13
What if we coulddo something more with all of this
‘CAPTCHA time’?
Tuesday 1 October 13
Tuesday 1 October 13
99.1%word-levelaccuracy
1.2bnCAPTCHASin year 1
440mwords
17mbooks
Tuesday 1 October 13
GAMES WITH A PURPOSE
In 2003 there were 9bn hours of solitaire played on PCs...
... and these days there are around 70m hours of FarmVille played every week!
It only took about 20m hours of human effort to build the Panama Canal!
Tuesday 1 October 13
FOLD.IT - MOLECULAR GAME PLAY
Tuesday 1 October 13
HOW WELL DOES IT ALL WORK?
In 2011, players of Foldit helped to decipher the crystal structure of the Mason-Pfizer monkey virus (M-PMV) retroviral protease, an AIDS-causing monkey virus.
Players “produced” an accurate 3D model of the enzyme in just 10 days! This structure had eluded scientists for some 15 years.
Khatib, F. et al. (2011). "Crystal structure of a monomeric retroviral protease solved by protein folding game players". Nature Structural & Molecular Biology 18 (10): 1175
Tuesday 1 October 13
BIG DATA OR BIG BROTHER?
Tuesday 1 October 13
THE END OF THE AGE OF PRIVACY?
“Technology is neither good nor bad, nor is it neutral”
Public by Default.
The Price of Free?
Ownership of Personal Data?
Tuesday 1 October 13
THE END OF ANONYMITY
The Case of AOL Searcher No. 4417749.
20M anonymized queries, 600k users as research data (AOL, 2006).
User No. 4417749 = 62 year old Thelma Arnold of Lilburn, Ga.
Tuesday 1 October 13
THE PANOPTICON STATE?
Zamyatin’s dystopian glass-walled future of government surveillance.
NSA Prism programme.
Tuesday 1 October 13
A shift in the data ownership model ⇒ a new asset class for personal data?
Owned by the individual ⇒ shared with services.
Cloud storage (e.g. DropBox) as a shareable repository of personal data...
CONTROLLING PERSONAL DATA
Tuesday 1 October 13
THE BIG DATA WORLD OF THE SENSOR WEB
Tuesday 1 October 13
N = ALLMESSYCORRELATION
Tuesday 1 October 13
THE OPTION-VALUE OF BIG DATADATA-DRIVEN EVERYTHINGPOWER TO THE PEOPLE
Tuesday 1 October 13
THE OPTION-VALUE OF BIG DATA
Reuse & Recycle
From Primary to Secondary Uses of Data
The Unintended Consequences of Data
Tuesday 1 October 13
DATA-DRIVEN EVERYTHING
Social Science, Linguistics, Anthropology, Cultural Studies, Journalism, Political Science, Humanities ...
All impacted by Big Data Thinking...
Tuesday 1 October 13
GOOGLE’S N-GRAM VIEWER
Acerbi A, Lampos V, Garnett P, Bentley RA (2013) The Expression of Emotions in 20th Century Books. PLoS ONE 8(3)
Tuesday 1 October 13
DATA-DRIVEN EVERYTHING
Michel J-P, Shen YK, Aiden AP, Veres A, Gray MK, et al. (2011) Quantitative analysis of culture using millions of digitized books. Science 331: 176–182
Lieberman E, Michel J-P, Jackson J, Tang T, Nowak MA (2007) Quantifying the evolutionary dynamics of language. Nature 449: 713–716
Richards, Daniel Rex. "The content of historical books as an indicator of past interest in environmental issues." Biodiversity and Conservation (2013): 1-9.
Lampos, Vasileios, et al. "Analysing Mood Patterns in the United Kingdom through Twitter Content." arXiv preprint arXiv:1304.5507 (2013).
Tuesday 1 October 13
POWER TO THE PEOPLE
Personal Data & Personal Analytics
People as Sensors in Participatory Sensing
Human Computation & Collective Intelligence
Tuesday 1 October 13
Creating a Data-Driven SocietyTuesday 1 October 13