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

1. SMALL SENSORS. BIG DATA.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

2. In a typical lifetime ... Tuesday 1 October 13

3. 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 TVTuesday 1 October 13

25. 1 = 10bytes 18 exabyte Tuesday 1 October 13

26. 1 = 10bytes 18 exabyte 1000,000,000,000,000,000 Tuesday 1 October 13

27. 1 = 10bytes 18 exabyte 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

28. 1 = 10bytes 18 exabyte 6 ! hours But, we now create this much information every Tuesday 1 October 13

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

30. A PARADIGM SHIFT algorithm data algorithm data Tuesday 1 October 13

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

32. computation sensors dev data Tuesday 1 October 13

33. 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 Tuesday 1 October 13

34. 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

35. Tuesday 1 October 13

36. A SELF-FULFILLING PROPHECY? CDC 6600 [1964] IBM PC [1981] iPHONE 5S [2013] 0.05 FLOPs/$ 200 FLOPs/$ 8M FLOPs/$ x4,000 x40,000 Tuesday 1 October 13

37. 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

38. 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

39. RAY KURZWEIL “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

40. 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

42. THE RISE OF THE SENSOR WEB Tuesday 1 October 13

43. 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

44. THE EMERGING SENSOR WEB Tabs Pads Boards Smart Sensors The Internet of Things Wearable Computing Tuesday 1 October 13

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

46. NOKIA’S MORPH CONCEPT DEVICE Tuesday 1 October 13

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

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

49. UNIVERSAL MOBILE SENSING PLATFORM Tuesday 1 October 13

50. UNIVERSAL MOBILE SENSING PLATFORM C A 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

51. Connectivity high-speed data Mobility location-aware Power always on Tuesday 1 October 13

52. 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

53. 7 YEARS3 MONTHS2 WEEKS 1 PERSON12M PHOTOS1TB Tuesday 1 October 13

54. Activities classiﬁcation summarisation Lifestyle behaviours preferences Events segmentation clustering Tuesday 1 October 13

56. 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

57. THERE’S AN APP FOR THAT Tuesday 1 October 13

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

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

60. MOOD & FOCUS The Melon Headband 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

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

62. 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

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

64. 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

65. 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

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

67. AUTOMATIC TACKLE CLASSIFICATION GPS + Accelerometer Tuesday 1 October 13

68. 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

69. ALWAYS ON MOBILE SENSING Tuesday 1 October 13

70. SC ALIN G Tuesday 1 October 13

71. vertical scalingTuesday 1 October 13

72. vertical scalingTuesday 1 October 13

73. horizontal scalingTuesday 1 October 13

74. horizontal scalingTuesday 1 October 13

75. PARTICIPATORYSENSINGTuesday 1 October 13

76. ASTHMOPOLIS SMART INHALER Tuesday 1 October 13

77. PARTICIPATORY SENSING Tuesday 1 October 13

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

79. 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

82. 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

83. 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

84. FROM REAL TO VIRTUAL SENSORS Tuesday 1 October 13

85. 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

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

87. 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

88. GOOGLE’S BIGGER IDEA $40billion 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

89. 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

90. SENSING DRUG SIDE- EFFECTS 82M Queries 6M Users Tuesday 1 October 13

91. 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. Tuesday 1 October 13

92. TURNING BROWSERS INTO BUYERS Understanding user preferences. Making personalized suggestions. Tuesday 1 October 13

93. items users Tuesday 1 October 13

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

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

96. MINING USER- GENERATED REVIEWS Tuesday 1 October 13

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

98. 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 ... Tuesday 1 October 13

99. Participatory sensing as collective intelligence Human Intelligence + Brute-Force Computation TOWARDS COLLECTIVE INTELLIGENCE Tuesday 1 October 13

100. 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

101. Yahoo! Mail CAPTCHA Tuesday 1 October 13

102. 250m CAPTCHAS PER DAY 150kPERSON-HOURS PER DAY 7mPERSON HOURS 45CAPTCHA DAYS! Tuesday 1 October 13

103. What if we could do something more with all of this ‘CAPTCHA time’? Tuesday 1 October 13

105. 99.1% word-level accuracy 1.2bn CAPTCHAS in year 1 440m words 17m books Tuesday 1 October 13

106. 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

107. FOLD.IT - MOLECULAR GAME PLAY Tuesday 1 October 13

108. 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

109. BIG DATA OR BIG BROTHER? Tuesday 1 October 13

110. 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

111. 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

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

113. 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

114. THE BIG DATA WORLD OF THE SENSOR WEB Tuesday 1 October 13

115. N = ALL MESSYCORRELATION Tuesday 1 October 13

116. THE OPTION-VALUE OF BIG DATA DATA-DRIVEN EVERYTHING POWER TO THE PEOPLE Tuesday 1 October 13

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

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

119. 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

120. 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

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

122. Creating a Data-Driven Society Tuesday 1 October 13

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

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