Small sensors-big-data-barry-smyth-ria-2013

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SMALL SENSORS. BIGDATA.FROM CLARITY TO INSIGHT IN THE WORLD OF THE SENSOR WEB Barry Smyth, INSIGHT Centre for Data Analytics @barrysmyth, barry.smyth@ucd.ie Tuesday 1 October 13

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In a typicallifetime ... Tuesday 1 October 13

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1 billion breaths2.5 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 TVTuesday 1 October 13

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1 = 10bytes 18 exabyte Tuesday1 October 13

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1 = 10bytes 18 exabyte 1000,000,000,000,000,000 Tuesday1 October 13

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1 = 10bytes 18 exabyte 20,000x all of the printed material in the US Library of Congress. Or all of the words spoken by humans. Ever! Tuesday 1 October 13

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1 = 10bytes 18 exabyte 6! hours But, we now create this much information every Tuesday 1 October 13

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Connecting atoms &bits. From algorithms to data. Tuesday 1 October 13

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A PARADIGM SHIFT algorithm data algorithm data Tuesday 1October 13

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VS IT’S NOT (JUST)ABOUT THE DATA N = all Messy & Diverse Reusable Correlation N = small Clean & Uniform Disposable Causation Tuesday 1 October 13

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computation sensors dev data Tuesday 1 October13

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THE WORLD’S FIRST SUPERCOMPUTER Brainchildof 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

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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

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A SELF-FULFILLING PROPHECY? CDC 6600 [1964] IBM PC [1981] iPHONE 5S [2013] 0.05 FLOPs/$ 200 FLOPs/$ 8M FLOPs/$ x4,000 x40,000 Tuesday1 October 13

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TO PUT THISINTO PERSPECTIVE ... The iPhone 5S is about 60,000 times more powerful that the Apollo 11’s guidance computer. Tuesday 1 October 13

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IF MOORE’S LAW APPLIEDTO 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

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RAY KURZWEIL “A computer thatonce 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

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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

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Tuesday 1 October13

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THE RISE OFTHE SENSOR WEB Tuesday 1 October 13

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UBIQUITOUS COMPUTING Mark Weiser’s 1988vision 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

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THE EMERGING SENSOR WEB Tabs Pads Boards SmartSensors The Internet of Things Wearable Computing Tuesday 1 October 13

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A MATERIALS SCIENCE DETOUR Chemistry& Physics Novel Materials & Structures Common Materials Next Generation Sensors Tuesday 1 October 13

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NOKIA’S MORPH CONCEPT DEVICE Tuesday1 October 13

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CHALLENGES OF PHYSICAL SENSING Conventionalsensors (thermistors, ﬂow meters, photoreceptors). Biofouling & Calibration. Robustness, Reliability, Energy & Communications. Cost, Cost, Cost. Tuesday 1 October 13

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SWEAT SENSING Microﬂuidic, Lab-on-a-Chip, Wearable. pHsensitive dye & photo-detector. Accurate, continuous, realtime Athletic performance Cystic Fibrosis Tuesday 1 October 13

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UNIVERSAL MOBILE SENSING PLATFORM Tuesday1 October 13

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UNIVERSAL MOBILE SENSING PLATFORM CA M E R A M I C R O P H O N ES P E E D L I G H T O R I E N T A T I O N H U M I D I T Y T E M P E R A T U R E L O C A T I O N T O U C H M O T I O N D I R E C T I O NF I N G E R P R I N T S Tuesday 1 October 13

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Connectivity high-speed data Mobility location-aware Power always on Tuesday1 October 13

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THE QUANTIFIED SELF MOVEMENT Adata-rich approach to everyday living. Gordon Bell (Microsoft) and the My Life Bits project Digitizing everyday life. SenseCam Tuesday 1 October 13

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7 YEARS3 MONTHS2WEEKS 1 PERSON12M PHOTOS1TB Tuesday 1 October 13

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Activities classiﬁcation summarisation Lifestyle behaviours preferences Events segmentation clustering Tuesday 1 October13

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THE DISRUPTION OF HEALTHCARE Always-onpersonal 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

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THERE’S AN APP FOR THAT Tuesday1 October 13

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EXERCISE & FITNESS Runkeeper iPhone/Android Running,Walking, Biking, ... Age, gender, weight, ... Location, pace, duration, climb, calories, heart rate,... Tuesday 1 October 13

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TRACKING SLEEP Basic ‘sleep tracking’ basedon motion. Duration vs Movement Sleep Quality (≈ time/move) Sleep Notes / Wakeup Moods Comparative Analytics Tuesday 1 October 13

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MOOD & FOCUS The MelonHeadband Uses EEG to track brain activity to assess ‘focus’. Tagging, location, and activity information helps users to better assess what impacts their focus. Tuesday 1 October 13

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FOOD & NUTRITION Meal loggingand nutritional analysis. Manual vs Semi-Automatic. Calorie goals and diet plans. Integrated weight tracking. Tuesday 1 October 13

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HEART RATE SENSING Usingsmartphone camera with your ﬁnger. No external sensor required. Detecting colour changes due to capillary blood-ﬂow. Tagging, comparative analytics etc Tuesday 1 October 13

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BLOOD GLUCOSE External blood glucosesensor automatically syncs readings with app. Readings tagged with mealtime, exercise etc. Analysis and visualization of trends, logs, stats. Tuesday 1 October 13

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MOBILE SPIROMETRY Using a mobilephone 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

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MOBILE SPIROMETRY Using a mobilephone 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

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SENSORS & SPORTS Profs BrianCaulﬁeld & Niall Moyna (@ CLARITY) Player Health vs Performance Analysis Rugby, Athletics, Cycling, Equestrian, Archery, Boxing, GAA, ... Tuesday 1 October 13

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AUTOMATIC TACKLE CLASSIFICATION GPS + Accelerometer Tuesday1 October 13

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CONSUMER-DRIVEN HEALTHCARE? Towards preventative, sensor-based, data-drivenhealthcare. Sparse checkups 24/7/365 Sensing The data is ours to share ... Apps vs Prescriptions? Tuesday 1 October 13

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ALWAYS ON MOBILE SENSING Tuesday 1October 13

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SC ALIN G Tuesday 1 October13

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vertical scalingTuesday 1October 13

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vertical scalingTuesday 1October 13

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horizontal scalingTuesday 1October 13

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horizontal scalingTuesday 1October 13

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PARTICIPATORYSENSINGTuesday 1 October13

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ASTHMOPOLIS SMART INHALER Tuesday 1October 13

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PARTICIPATORY SENSING Tuesday 1 October13

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HACKING YOUR COMMUTE GPS &Navigation Assistants Map Apps Rule the World TomTom, Garmin, Google, Apple, Nokia, ... Tuesday 1 October 13

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CROWDSOURCED MAPPING (WAZE) Free smartphoneapp. Real-time sensing of users’ location, time, speed etc. x millions of users = social mapping + trafﬁc ﬂow, alerts, hazards, ... Tuesday 1 October 13

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CITIZEN SENSING PUBLIC TRANSPORT Roadify(iPhone App) Status updates for public transport experiences. Train, bus, subway, ferry, parking, ... Opinions Alerts, Recommendations, Delays, ... Tuesday 1 October 13

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TURNING PEOPLE INTO SENSORS Participatory/CitizenSensing 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

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FROM REAL TO VIRTUAL SENSORS Tuesday1 October 13

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MINING THE DATA EXHAUST FromReal 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

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THE ORIGINAL BIGDATA COMPANY Mining relevance & reputation from links. Search logs as sensor data. Tuesday 1 October 13

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PAGERANK GOOGLE’S BIG IDEA Theimportance 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

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GOOGLE’S BIGGER IDEA $40billion Google’s realBigger 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

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SEARCH LOGS AS SENSORDATA “... 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

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SENSING DRUG SIDE- EFFECTS 82M Queries 6M Users Tuesday1 October 13

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SENSING FLU TRENDS Identiﬁed triggerterms 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. Tuesday 1 October 13

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TURNING BROWSERS INTO BUYERS Understandinguser preferences. Making personalized suggestions. Tuesday 1 October 13

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items users Tuesday 1 October13

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items users Correlations between theratings patterns of users denote user similarity ... People like you have also liked ... Tuesday 1 October 13

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items users Conversely correlations between theratings patterns of items denote item similarity ... If you liked X then you might like Y... Tuesday 1 October 13

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MINING USER- GENERATED REVIEWS Tuesday1 October 13

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USER-GENERATED REVIEWS +‘ves staff location bed service breakfast -‘ves noise elevators carpet health club public transport ChicagoHotels Tuesday 1 October 13

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OPINION AMPLIFICATION Twitter, FaceBook asa 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 ... Tuesday 1 October 13

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Participatory sensing ascollective intelligence Human Intelligence + Brute-Force Computation TOWARDS COLLECTIVE INTELLIGENCE Tuesday 1 October 13

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DEALING WITH EMAIL SPAM Backin 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

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Yahoo! Mail CAPTCHA Tuesday1 October 13

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250m CAPTCHAS PER DAY 150kPERSON-HOURS PER DAY 7mPERSON HOURS 45CAPTCHA DAYS! Tuesday1 October 13

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What if wecould do something more with all of this ‘CAPTCHA time’? Tuesday 1 October 13

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99.1% word-level accuracy 1.2bn CAPTCHAS in year 1 440m words 17m books Tuesday1 October 13

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GAMES WITH A PURPOSE In2003 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

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FOLD.IT - MOLECULAR GAMEPLAY Tuesday 1 October 13

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HOW WELL DOESIT 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

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BIG DATA OR BIGBROTHER? Tuesday 1 October 13

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THE END OFTHE 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

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THE END OF ANONYMITY TheCase 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

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THE PANOPTICON STATE? Zamyatin’s dystopianglass-walled future of government surveillance. NSA Prism programme. Tuesday 1 October 13

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A shift inthe 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

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THE BIG DATAWORLD OF THE SENSOR WEB Tuesday 1 October 13

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N = ALL MESSYCORRELATION Tuesday1 October 13

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THE OPTION-VALUE OFBIG DATA DATA-DRIVEN EVERYTHING POWER TO THE PEOPLE Tuesday 1 October 13

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THE OPTION-VALUE OF BIGDATA Reuse & Recycle From Primary to Secondary Uses of Data The Unintended Consequences of Data Tuesday 1 October 13

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DATA-DRIVEN EVERYTHING Social Science, Linguistics,Anthropology, Cultural Studies, Journalism, Political Science, Humanities ... All impacted by Big Data Thinking... Tuesday 1 October 13

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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) Tuesday 1 October 13

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DATA-DRIVEN EVERYTHING Michel J-P, ShenYK, 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

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POWER TO THEPEOPLE Personal Data & Personal Analytics People as Sensors in Participatory Sensing Human Computation & Collective Intelligence Tuesday 1 October 13

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Creating a Data-DrivenSociety Tuesday 1 October 13

Small sensors-big-data-barry-smyth-ria-2013

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Small sensors-big-data-barry-smyth-ria-2013