small sensors. big data. - iiea.com · small sensors. big data. prof. barry smyth insight centre...
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Small Sensors. Big Data.
Prof. Barry Smyth Insight Centre for Data Analytics University College Dublin !e: [email protected] t: @barrysmyth
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Data, data, everywhere…
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1 = 10bytes18exabyte
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1 = 10bytes18exabyte
1000,000,000,000,000,000
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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!
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1 = 10bytes18exabyte
6 !hours
But, we now create this much information every
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A PARADIGM SHIFT
algorithm !
data
algorithm !
data
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computation
sensorsdev
data
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THE WORLD’S FIRST SUPERCOMPUTER
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, $60m
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MOORE’S MAGICAL LAWS
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A SELF-FULFILLING PROPHECY?
CDC 6600
[1964]
IBMPC [1981]
iPHONE 5S [2013]
0.05FLOPs/$
200FLOPs/$
8MFLOPs/$
x4,000 x40,000
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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 than to park it downtown for an evening.” (Carver Mead)
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THE RISE OF THE SENSOR WEB
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UNIVERSAL MOBILE SENSING PLATFORM
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UNIVERSAL MOBILE SENSING PLATFORM
C A M E R A
M ICROPHONES 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
L OCATION
TOUCH
MOTION
DIRECT IONF I N G E R P R I N T S
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Connectivity high-speed data
Mobility location-aware
Power always on
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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, ... !
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THERE’S AN APP FOR THAT
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EXERCISE & FITNESS
Runkeeper iPhone/Android !
Running, Walking, Biking, ... !
Age, gender, weight, ... !
Location, pace, duration, climb, calories, heart rate,...
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TRACKING SLEEP
Basic ‘sleep tracking’ based on motion. !
Duration vs Movement !
Sleep Quality (≈ time/move) !
Sleep Notes / Wakeup Moods !
Comparative Analytics
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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.
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FOOD & NUTRITION
Meal logging and nutritionalanalysis. !
Manual vs Semi-Automatic. !
Calorie goals and diet plans. !
Integrated weight tracking.
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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
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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.
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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.
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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?
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ALWAYS ON MOBILE SENSING
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SCALING
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vertical scaling
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horizontal scaling
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horizontal scaling
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PARTICIPATORYSENSING
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ASTHMOPOLIS SMART INHALER
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PARTICIPATORY SENSING
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HACKING YOUR COMMUTE
GPS & Navigation Assistants !
Map Apps Rule the World !
TomTom, Garmin, Google, Apple, Nokia, ... !
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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, ...
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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 to them.
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FROM REAL TO VIRTUAL SENSORS
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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, ...
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THE ORIGINAL BIG DATA COMPANY
Mining relevance & reputation from links. !
Search logs as sensor data.
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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)
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SENSING DRUG SIDE-EFFECTS
82M Queries
6M Users
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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 verified CDC data.
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MINING USER-GENERATED REVIEWS
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USER-GENERATED REVIEWS
+‘ves staff
location bed
service breakfast
-‘ves noise elevators carpet health club public transport
Chicago Hotels
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OPINION AMPLIFICATION
Twitter, FaceBook as a source of real-time opinions. !
Raw Text ⇒ Sentiment ⇒ Opinion !
These days Twitter data has been used to predict election outcomes, box office success, and musical talent ...
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DATA-DRIVEN EVERYTHING
Social Science, Linguistics, Anthropology, Cultural Studies, Journalism, Political Science, Humanities ... !
All impacted by Big Data Thinking...
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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)
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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).
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POWER TO THE PEOPLE
The Big Data Revolution is here today! !
Transformation ⇒ Disruption ⇒ Opportunity !
People as Sensors ⇒ Collective Intelligence !
Personal Data, Privacy, Ethics …
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SMALL SENSORS. BIG DATA. FROM CLARITY TO INSIGHT IN THE WORLD OF THE SENSOR WEB
Creating a Data-Driven Society
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Small Sensors. Big Data.
Prof. Barry Smyth Insight Centre for Data Analytics University College Dublin !e: [email protected] t: @barrysmyth