Quantified Self Ideology: Personal Data becomes Big Data

11,436 views

Published on

A key contemporary trend emerging in big data science is the quantified self: individuals engaged in the deliberate self-tracking of any kind of biological, physical, behavioral, or transactional information, as n=1 individuals or in groups. The quantified self is one dimension of the bigger trend to integrate and apply a variety of personal information streams including big health data (genome, transcriptome, environmentome, diseasome), quantified self data streams (biosensor, fitness, sleep, food, mood, heart rate, glucose tracking, etc.), traditional data streams (personal and family health history, prescription history) and IOT (Internet of things) activity data streams (smart home, smart car, environmental sensors, community data). This talk looks at how personal data and group data are becoming big data as individuals and communities share, collaborate, and work with large personalized data sets using novel discovery methods such as anomaly detection and exception reporting, longitudinal baseline analysis, episodic triggers, and hierarchical machine learning.

Published in: Technology, Business
2 Comments
15 Likes
Statistics
Notes
No Downloads
Views
Total views
11,436
On SlideShare
0
From Embeds
0
Number of Embeds
2,090
Actions
Shares
0
Downloads
255
Comments
2
Likes
15
Embeds 0
No embeds

No notes for slide

Quantified Self Ideology: Personal Data becomes Big Data

  1. 1. 7 February 2014 Université Paris Descartes, Paris France Slides: http://slideshare.net/LaBlogga Melanie Swan m@melanieswan.com Philosophy of Big Data and Quantified Self: Personal Data becomes Big Data
  2. 2. 7 February 2014 QS Big Data 2 About Melanie Swan  Founder DIYgenomics, science and technology innovator and philosopher  Singularity University Instructor, IEET Affiliate Scholar, EDGE Contributor  Education: MBA Finance, Wharton; BA French/Economics, Georgetown Univ  Work experience: Fidelity, JP Morgan, iPass, RHK/Ovum, Arthur Andersen  Sample publications: Source: http://melanieswan.com/publications.htm  Kido T, Kawashima M, Nishino S, Swan M, Kamatani N, Butte AJ. Systematic Evaluation of Personal Genome Services for Japanese Individuals. Nature: Journal of Human Genetics 2013, 58, 734-741.  Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.  Swan, M. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012, 1(3), 217-253. Swan, M. Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen. J Pers Med 2012, 2(3), 93-118.  Swan, M. Steady advance of stem cell therapies. Rejuvenation Res 2011, Dec;14(6):699-704.  Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet Med 2010, May;12(5):279-88.
  3. 3. 7 February 2014 QS Big Data Progress of TechnoHuman Evolution 3
  4. 4. 7 February 2014 QS Big Data 4 Data Big Data!
  5. 5. 7 February 2014 QS Big Data 5 Inspired by: Average is Over, Tyler Cowen, 2013: Decline of knowledge worker jobs due to machine intelligence more efficiently performing 75% of tasks; optimal mix is 75% machine + 5% human Human’s Role in the World is Changing
  6. 6. 7 February 2014 QS Big Data Conceptualizing Big Data Categories 6 Personal Data Group Data Tension: Individual vs Institution Sense of data belonging to a group Open Data
  7. 7. 7 February 2014 QS Big Data Agenda  Personal Data  Quantified Self  Quantified Self and Big Data  Advanced QS Concepts  Group Data  Urban Data  Conclusion 7
  8. 8. 7 February 2014 QS Big Data What is the Quantified Self? 8  Individual engaged in the self- tracking of any kind of biological, physical, behavioral, or environmental information  Data acquisition through technology: wearable sensors, mobile apps, software interfaces, and online communities  Proactive stance: obtain and act on information Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  9. 9. 7 February 2014 QS Big Data Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API (Sano Intelligence), Continuous Monitors (Medtronic) 9 Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre), Scanaflo Urinalysis1 QS Sensor Mania! Wearable Electronics Source: Swan, M. Sensor Mania! J Sens Actuator Netw 2012. 1 Glucose, protein, leukocytes, nitrates, blood, bilirubin, urobilinogen, specific gravity, and pH urinalysis Increasingly continuous and automated data collection
  10. 10. 7 February 2014 QS Big Data Wearables: a Platform and an Ecosystem 10 Smart Gadgetry Creates Continuous Personal Information Climate PC/Tablet/Cloud SmartphoneNew Wearable Platforms: Smartwatch, AR/Glass, Contacts AR = Augmented Reality
  11. 11. 7 February 2014 QS Big Data Miniaturization: BioSensor Electronic Tattoos 11 Source: http://www.jacobsschool.ucsd.edu/pulse/winter2013/page3.shtml#tattoos Electrochemical Sensors Tactile Intelligence: Haptic Data Glove Chemical Sensors Disposable Electronics Wearable Electronics: Detect External BioChemical Threats and Track Internal Vital Signs
  12. 12. 7 February 2014 QS Big Data Quantified Self Worldwide Community  Goal: personalized knowledge through quantified self-tracking  ‘Show n tell’ meetups  What did you do? How did you do it? What did you learn? 12 Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012. Videos, Conferences, Meetup Groups
  13. 13. 7 February 2014 QS Big Data 13 Source: http://www.meetup.com/QSParis/, http://www.meetup.com/ParisGlassUG/
  14. 14. 7 February 2014 QS Big Data 14 Quantified Self Project Examples  Low-cost home-administered blood, urine, saliva tests OrSense continuous non-invasive glucose monitoring Cholestech LDX home cholesterol test ZRT Labs dried blood spot tests  Food consumption (1 yr)1 and the Butter Mind study2  Study 1 Source: http://flowingdata.com/2011/06/29/a-year-of-food-consumption-visualized 2 Source: http://quantifiedself.com/2011/01/results-of-the-buttermind-experiment
  15. 15. 7 February 2014 QS Big Data Quantified Self Measurements… 15 1 METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-that- perfect-quantified-self-app-notes-to-developers-and-qs-community-html/  Physical Activities  Miles, steps, calories, repetitions, sets, METs1  Diet and Nutrition  Calories consumed, carbs, fat, protein, specific ingredients, glycemic index, satiety, portions, supplement doses, tastiness, cost, location  Psychological, Mental, and Cognitive States and Traits  Mood, happiness, irritation, emotion, anxiety, esteem, depression, confidence  IQ, alertness, focus, selective/sustained/divided attention, reaction, memory, verbal fluency, patience, creativity, reasoning, psychomotor vigilance  Environmental Variables  Location, architecture, weather, noise, pollution, clutter, light, season  Situational Variables  Context, situation, gratification of situation, time of day, day of week  Social Variables  Influence, trust, charisma, karma, current role/status in the group or social network
  16. 16. 7 February 2014 QS Big Data The Quantified Self is Mainstream 16  Self-tracking statistics (Pew Research Center)  60% US adults track weight, diet, or exercise  33% US adults monitor blood sugar, blood pressure, headaches, or sleep patterns  9% receive text message health alerts  40,000 smartphone health applications  QS thought leadership  Press : BBC, Forbes, and Vanity Fair  Electronics show focus at CES 2013  Health 2.0: “500+ companies making self-management tools; VC funding up 20%” Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  17. 17. 7 February 2014 QS Big Data QS Experimentation Motivation and Features 17 Source: DIYgenomics Knowledge Generation through Self-Experimentation Study http://genomera.com/studies/knowledge-generation-through-self-experimentation  DIYgenomics QS Study (n=37)  Desired outcome: optimality and improvement (vs pathology resolution)  Personalized intervention for depression, low energy, sleep quality, productivity, and cognitive alertness  Rapid experimental iteration through solutions and kinds of solutions  Resolution point found within weeks  Pragmatic problem-solving focus, little introspection
  18. 18. 7 February 2014 QS Big Data 18 Source: http://www.DIYgenomics.org http://genomera.com/studies/dopamine-genes-and-rapid-reality-adaptation-in-thinking
  19. 19. 7 February 2014 QS Big Data History of the Quantified Self 19  Sanctorius of Padua 16th c: energy expenditure in living systems; 30 years of QS weight/food data  QS Philosophers  Epicureans, Heidegger, Foucault): ‘care of the self’  ‘Self’: recent concept of modernity  QS: contemporary formalization using measurement, science, and technology to bring order and control to the natural world, including the human body Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  20. 20. 7 February 2014 QS Big Data Sensor Mania! QS Gadgetry Trend 20 Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.
  21. 21. 7 February 2014 QS Big Data 21 Wireless Internet-of-Things (IOT) Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. Image credit: Cisco
  22. 22. 7 February 2014 QS Big Data 6 bn Current IOT devices to double by 2016 22 Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T 3 year doubling cycle
  23. 23. 7 February 2014 QS Big Data IOT World of Smart Matter  IOT Definition: digital networks of physical objects linked by the Internet that interact through web services  Usual gadgetry (e.g.; smartphones, tablets) and now everyday objects: cars, food, clothing, appliances, materials, parts, buildings, roads  Embedded microprocessors in 5% human-constructed objects (2012)1 23 1 Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012. http://singularitysummit.com/schedule
  24. 24. 7 February 2014 QS Big Data IOT Contributing to Explosion of Big Data  Big Data definition: data sets too large and complex to process with on-hand database management tools (volume, velocity, variety)  Examples  Walmart : 1 million transactions/hr transmitted to 3 PB database  BBC: 7 PB video served/month from 100 PB physical disk space  Structured and unstructured data  Big data is not smart data  Discarded, irretrievable 24 Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics
  25. 25. 7 February 2014 QS Big Data Networked Sensing – New Topology 25 Machine:Machine VL Sensor Networks Internet of Things 6LoWPANS Human:Human Telephone System (POTS) Human:Machine Machine:Machine Internet Protocol Packet Switching Unprecedented Scale Requires New Communications Protocols
  26. 26. 7 February 2014 QS Big Data Basis for Networked Sensing Protocols 26 Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic Biomimicry, Synthetic Biology Fish, Hive, Swarm Turbulence, Chaos, Perturbation
  27. 27. 7 February 2014 QS Big Data 27  Annual data creation in zettabytes (10007 bytes)  90% of the world’s data created in the last 2 years  Sectors: personal, corporate, government, scientific Defining Trend of Current Era: Big Data Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trends http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf 2 year doubling cycle
  28. 28. 7 February 2014 QS Big Data Typical Big Data Problems  Perform sentiment analysis on 12 terabytes of daily Tweets  Predict power consumption from 350 billion annual meter readings  Identify potential fraud in a business’s 5 million daily transactions 28 http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx
  29. 29. 7 February 2014 QS Big Data QS is inherently a Big Data problem 29  Data collection, processing, analysis  Cloud computing for consumer processing  Local computing tools are not available to store, query, and manipulate QS data sets  Cloud-based analysis: Predictive modeling, natural-language processing, machine learning algorithms over very-large data sets of heterogeneous data  Rapid growth in QS data sets  Manually-tracked ‘small data’ is now automatically-collected ‘big data’  Excel -> Hadoop  Macros -> MapReduce/Mahout Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  30. 30. 7 February 2014 QS Big Data QS Big Data Challenge Predictive Cardiac Risk Monitoring 30 Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.  Heart rate monitor sampling  250 times per second  9 gigabytes of data per person per month  Cardiac events can be predicted two weeks ahead of time  Phase I:  Collect, store, process, analyze data  Compression and search algorithms  Identify event triggers  Phase II  Predict and intervene with low false-positives
  31. 31. 7 February 2014 QS Big Data QS Big Data: Personal Health ‘Omics’ 31 DNA: SNP mutations Microbiomics Proteomics RNA expression profiling Epigenetics Health 2.0: Personal Health Informatics DNA: Structural variation Metabolomics Source: Academic papers re: integrated health data streams: Auffray C, et al. Looking back at genomic medicine in 2011. Genome Med. 2012 Jan 30;4(1):9. Chen R et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012 Mar 16;148(6):1293-307.
  32. 32. 7 February 2014 QS Big Data QS Big Data: Personal Information Streams Genome: SNP mutations Structural variation Epigenetics Microbiome Transcriptome Environmentome Metabolome Diseasome Proteome Personal and Family Health History Prescription History Lab Tests: History and Current Demographic Data Self-reported data: health, exercise, food, mood journals, etc. Biosensor Data Objective Metrics Quantified Self Device Data Mobile App Data Quantified SelfTraditional‘Omics’ Standardized Questionnaires Legend: Consumer-available 32 Personal Robotics Smart Car Smart Home Environmental Sensors Internet-of-Things Community Data 32Source: Kido, Swan, et al. Systematic evaluation of personal genome services. Nature: Journal of Human Genetics (2013) 58, 734–741.
  33. 33. 7 February 2014 QS Big Data APIs and Multi-QS Data Stream Integration 33
  34. 34. 7 February 2014 QS Big Data Fluxstream Unified QS Dashboard 34 Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/
  35. 35. 7 February 2014 QS Big Data Sen.se Integrated QS Dashboard 35 Source: http://blog.sen.se/post/19174708614/mashups-turning-your-data-into- something-useable-and  ‘Mulitviz’ display: investigate correlation between coffee consumption, social interaction, and mood
  36. 36. 7 February 2014 QS Big Data Wholly different concept and relation to data  Formerly everything signal, now 99% noise  Medium of big data opens up new methods:  Exception, characterization, variability, pattern recognition, correlation, prediction, early warnings  Big Data causality is ‘quantum mechanical’  Allows attitudinal shift to active from reactive  Two-way communication: biometric variability in the translates to to real-time recommendations  Example: degradation in sleep quality and hemoglobin A1C levels predict diabetes onset by 10 years1 36 1 Source: Heianza et al. High normal HbA(1c) levels were associated with impaired insulin secretion. Diabet Med 2012. 29:1285-1290.
  37. 37. 7 February 2014 QS Big Data Big Data opens up new Methods  Google: large corpora and simple algorithms  Foundational characterization (previously unavailable)  Longitudinal baseline measures of internal and external daily rhythms, normal deviation patterns, contingency adjustments, anomaly, and emergent phenomena  New kinds of Pattern Recognition (different structures)  Analyze data in multiple paradigms: time, frequency, episode, cycle, and systemic variables (transaction, experience, behavior)  New trends, cyclicality, episodic triggers, and other elements that are not clear in traditional time-linear data  Multi-disciplinarity  Turbulence, topology, chaos, complexity, etc. models 37 Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  38. 38. 7 February 2014 QS Big Data Opportunity: QS Data Commons  Common repository for personal informatics data streams  Fitbit, Jawbone UP, Nike, Withings, myZeo, 23andMe, Glass, Pebble, Basis, BodyMedia  Architecting consumer-friendly models  Open-access databases, developer APIs, front- end web services and mobile apps  (Precedent: public genotype/phenotype data)  Accommodate multi-tier privacy standards  Ecosystem value propositions: service providers, research community, biometric data-owners  Role of public and private service providers 38 Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  39. 39. 7 February 2014 QS Big Data Github: de facto QS Data Commons 39 Source: https://github.com/beaugunderson/genome
  40. 40. 7 February 2014 QS Big Data QS Frontier: Mental Performance Optimization 40 ‘Siri 2.0’ Personal Virtual Coach from DIYgenomics Sources: http://cbits.northwestern.edu and http://quantifiedself.com/2009/03/a-few-weeks-ago-i Source: DIYgenomics Social Intelligence Study http://diygenomics.pbworks.com/w/page/48946791/social_intelligence PTSD App Mood Management Apps from Mobilyze and M. Morris Source: http://www.ptsd.va.gov/pu blic/pages/ptsdcoach.asp
  41. 41. 7 February 2014 QS Big Data Next-gen QS Services: Quality of Life 41 QS Aspiration Apps: Happiness, Emotive State (personal and group), Well-being, Goal Achievement Category and Name Website URL Happiness Tracking Track Your Happiness http://www.trackyourhappiness.org/ Mappiness http://www.mappiness.org.uk/ The H(app)athon Project http://www.happathon.com/ MoodPanda http://moodpanda.com/ TechurSelf http://www.techurself.com/urwell Emotion Tracking and Sharing Gotta Feeling http://gottafeeling.com/ Emotish http://emotish.com/ Feelytics http://feelytics.me/ Expereal http://expereal.com/ Population-level Emotion Barometers We Feel Fine http://wefeelfine.org/ moodmap http://themoodmap.co.uk/ Pulse of the Nation http://www.ccs.neu.edu/home/amislove/twittermood/ Twitter Mood Map http://www.newscientist.com/blogs/onepercent/2011/09/twitt er-reveals-the-worlds-emo-1.html Wisdom 2.0 http://wisdom2summit.com/ Personal Wellbeing Platforms GravityEight http://www.gravityeight.com/ MindBloom https://www.mindbloom.com/ Get Some Headspace http://www.getsomeheadspace.com/ Curious http://wearecurio.us/ uGooder http://www.ugooder.com/ Goal Achievement Platforms uMotif http://www.uMotif.com/ DidThis http://blog.didthis.com/ Schemer https://www.schemer.com/ (personalized recommendations) Pledge/Incentive-Based Goal Achievement Platforms GymPact http://www.gym-pact.com/ Stick http://www.stickk.com/ Beeminder https://www.beeminder.com/ Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  42. 42. 7 February 2014 QS Big Data Next-gen QS Services: Behavior Change 42 Source: http://askmeevery.com/
  43. 43. 7 February 2014 QS Big Data Next-gen QS Services: Behavior Change  Shikake: Sensors embedded in physical objects to trigger a physical or psychological behavior change  Examples:  Transparent trash cans  Trash cans playing an appreciative sound to encourage litter to be deposited  Stairs light up on approach  Appreciative ping/noise from QS gadgetry 43 Source: http://mtmr.jp/en/papers/taai2013v2.pdf
  44. 44. 7 February 2014 QS Big Data Next-gen QS Services: 3D Quantification 44 BodyMetrics and Poikos: Fitness and Clothing Customization Apps OMsignal: Smart Apparel 24/7 Biometric Monitoring
  45. 45. 7 February 2014 QS Big Data 45  Sense of ourselves as information generators in constant dialogue with the pervasive information climate  Subject and environment co-create (Baudelaire’s detached flâneur observing the modern city); now data is the co-producing environment Subjectivation: The TechnoBioCitizen Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  46. 46. 7 February 2014 QS Big Data 46 Magnetic Sense: Finger and Arm Magnets North Paw Haptic Compass Anklet and Heart Spark http://www.youtube.com/watch?v=D4shfNufqSg http://sensebridge.net/projects/heart-spark Extending our senses in new ways to perceive data as sensation Serendipitous Joy: Smile- triggered EMG muscle sensor with an LED headband display Building Exosenses for the Qualified Self Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.
  47. 47. 7 February 2014 QS Big Data Exosenses: Quantified Intermediates  Networked quantified intermediates for human senses: smarter, visible, sharable through big data processing  Vague sense of heart rate variability, blood pressure; haptically-available exosenses make the data explicit  Haptics, audio, visual, taste, olfactory mechanisms to make metrics explicit: heart rate variability, blood pressure, galvanic skin response, stress level  Skill as exosense: technology as memory, self-experimentation as a form of exosense 47 Gut-on-a-chip Lung-on-a-chip Source: web.mit.edu/newsoffice/2012/human-body-on-a-chip-research-funding-0724.html Nose-on-a-chip Chip-on-a-Ring
  48. 48. 7 February 2014 QS Big Data QS Big Data Frontier: Neural Tracking 24/7 Consumer EEG, Eye-tracking, Emotion-Mapping, Augmented Reality Glasses 48 Consumer EEG Rigs 1.0 2.0 Augmented Reality Glasses Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.
  49. 49. 7 February 2014 QS Big Data QS Big Data Frontier: DIYneuroscience 49 http://www.diygenomics.org/files/DIYneuroscience.pdf https://www.facebook.com/DIYneuroscience
  50. 50. 7 February 2014 QS Big Data 50 QS Big Data: Biocitizen is Locus of Action Individual 2. Peer collaboration and health advisors Health social networks, crowdsourced studies, health advisors, wellness coaches, preventive care plans, boutique physicians, genetics coaches, aestheticians, medical tourism 3. Public health system Deep expertise of traditional health system for disease and trauma treatment 1. Continuous health information climate Automated digital health monitoring, self-tracking devices, and mobile apps providing personalized recommendations Source: Extended from Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 2009, 2, 492-525.
  51. 51. 7 February 2014 QS Big Data Conceptualizing Big Data Categories 51 Personal Data Group Data Tension: Individual vs Institution Sense of data belonging to a group Open Data
  52. 52. 7 February 2014 QS Big Data Agenda  Personal Data  Quantified Self  Quantified Self and Big Data  Advanced QS Concepts  Group Data  Urban Data  Conclusion 52
  53. 53. 7 February 2014 QS Big Data 53 Group Data: Smart City, Future City Image: http://www.sydmead.com
  54. 54. 7 February 2014 QS Big Data Global Population: Growing and Aging 54 Source: UN Habitat – 2010 http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html
  55. 55. 7 February 2014 QS Big Data 3 billion new Internet users by 2020 55 Source: Peter Diamandis Singularity University
  56. 56. 7 February 2014 QS Big Data  Over 50% worldwide population in 2008  5 billion in 2030 (estimated)  Megacity: (>10 million and possibly 2,000/km2 ) Human Urbanization: Living in Cities 56
  57. 57. 7 February 2014 QS Big Data 57 Megacity Growth Rates Source: Wikipedia
  58. 58. 7 February 2014 QS Big Data Big Urban Data: Killer Apps 58 Source: Copenhagen Pollution Levels, MIT Senseable City Lab  Public transit, traffic management, eTolls, parking, adaptive lighting, smart waste, pest control, hygiene management, asset tracking, smart power grid
  59. 59. 7 February 2014 QS Big Data Data Signature of Humanity 59 Source: http://senseable.mit.edu/signature-of-humanity/ MIT SENSEable City Lab – the Real-Time City  Flexible services responding predictively to individual and community-level demand (ex: pedestrian load)
  60. 60. 7 February 2014 QS Big Data Urban Data: 3D Buildings + Population Density 60 Source: ViziCities
  61. 61. 7 February 2014 QS Big Data 3D Tweet Landscape, ODI Chips 61 Source: http://vimeo.com/67872925 http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl
  62. 62. 7 February 2014 QS Big Data 3D Urban Data Viz: Decision-making Tool 62 Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/
  63. 63. 7 February 2014 QS Big Data Group Data: Office Building Community 63 Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010
  64. 64. 7 February 2014 QS Big Data Himalayas Water Tower Big Data 3D Printed Dwellings of the Future Living Treehouses – Mitchell Joachim Masdar, Abu Dhabi – Energy City of the Future
  65. 65. 7 February 2014 QS Big Data Agricultural Innovation: Vertical Farms, Tissue-Engineered Meat 65 San Diego, California (planned) Singapore (existing) Modern Meadow (existing)1 1 Source: http://www.popsci.com/article/science/can-artificial-meat-save-world
  66. 66. 7 February 2014 QS Big Data Reconfiguration of Space: Seasteading 66
  67. 67. 7 February 2014 QS Big Data Transportation Revolution 67 Solar Power: Tesla + Solar City Self-Driving CarPersonalized Pod Transport Source: Google's Self-Driving Cars Complete 300K Miles Without Accident, Deemed Ready for Commuting http://techcrunch.com/2012/08/07/google-cars-300000-miles-without-accident/
  68. 68. 7 February 2014 QS Big Data Another Pervasive Trend: Crowdsourcing 68 Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw
  69. 69. 7 February 2014 QS Big Data Crowd Models Extend to all Sectors  Crowdsourcing: coordination of large numbers of individuals (the crowd) through an open call on the Internet in the conduct of some sort of activity  Economics: crowdsourced labor marketplaces, crowdfunding, grouppurchasing, data competition (Kaggle)  Politics: flashmobs, organizing, opinion-shifting, data-mining  Social: blogs, social networks, meetup, online dating  Art & Entertainment: virtual reality, multiplayer games  Education: MOOCs (massively open online courses)  Health: health social networks, digital health experimentation communities, quantified self  Digital public goods: Wikipedia, online health databanks, data commons resources, crowdscience competitions 69
  70. 70. 7 February 2014 QS Big Data Agenda  Personal Data  Quantified Self  Quantified Self and Big Data  Advanced QS Concepts  Group Data  Urban Data  Conclusion 70
  71. 71. 7 February 2014 QS Big Data But wait…Limitations and Risks  Transition to access not ownership models  Data rights and responsibilities  Personal data and group data  Regulatory and policy tensions  Surveillance (top-down) vs souveillance (bottom-up)  Multi-tier privacy and sharing preferences  Digital divide accessibility, non-discrimination  Precedent: Uninformed consumer with lack of access (e.g.; health data, genomics, credit scoring)  Consumer non-adoption, ease-of-use, social acceptance, value propositions, financial incentive 71
  72. 72. 7 February 2014 QS Big Data  Increasingly a Foucauldian surveillance society  Downside: NSA surveillance of citizens sans recourse  Upside: continual biomonitoring for preventive medicine  Mindset shifts and societal maturation  Honesty about true desires (Deleuze’s desiring production)  Reduce shame: needs tend to be singular not individual  Wikipedia (1% open participation, 99% benefits)  Radical openness Evolving Shape of #1 Concern: Privacy 72 Privacy
  73. 73. 7 February 2014 QS Big Data Proliferation of New QS Big Data Flows  QS Device Data  Biometric data (HRM), personal genomic data  Personal medical and health data  QS neural-tracking, eye-tracking, affect data  Personal IOT Data  Cell phone, wearable electronics data  Smartphone digital identity & payment  Personal Urban Data  Smart home, smart car  Smart city data (e.g.; transportation)  Personal Robotics Data 73
  74. 74. 7 February 2014 QS Big Data Top 10 QS Big Data Trends Internet-of-Things (IOT) Sensor Networks 3 billion New People Online 3D Information Visualization Megacity Growth Smart City Future City QS Device Ecosystem Crowdsourcing Self-Empowerment DIY Attitude 74 Wearable Electronics Urban Data Biocitizen Personal Data Group Data
  75. 75. 7 February 2014 QS Big Data QS Big Data Summary  Next-gen QS services  Wearable Electronics as the QS platform  Improve quality of life, facilitate behavior change  IOT continuous personal information climates  QS Big Data  Wholly different relation to data: 99% noise  Rights and responsibilities model of data access  Group Data  Megacity growth, urban data flow, 3 bn coming online  Personal Data  Technology-enabled biocitizen self-produces in the data environment and takes action 75
  76. 76. 7 February 2014 QS Big Data 76 The Philosophy of Big Data Centrally about our relation to technology: Our attunement to technology as an enabling background helps us see the possibilities for the true meaningfulness of our being - Heidegger Source: Heidegger, M. The Question Concerning Technology, 1954; Derrida, J. Paper Machine, 2005 The thinking of the event (organic, singular) is joined to the thinking of the machine (inorganic, repetition), where the new logic is the virtualization of the event by the machine, a virtuality that extends the classical opposition of the possible and the impossible - Derrida
  77. 77. 7 February 2014 QS Big Data  Apply philosophical principles to modern technology Technology Futures Institute 77 http://melanieswan.com/TFI.html Ontology Existence Subjectivation Ethics Aesthetics Valorization Meaning-making Reality Language Big Data Wearables Surveillance Society Synthetic Biology Bioart Biohacking NanoCognition Cognitive Enhancement 3D Printing http://melanieswan.com/TFI.html
  78. 78. 7 February 2014 QS Big Data Technology Futures Institute  Mission: use philosophy to improve the rigor of our thinking about science and technology  Sample Projects  Ethics of Perception in Nanocognition – Perception is a feature (Glass, electronic contacts, nanorobotic cognitive aids), not an evolutionary given, therefore how do we want to perceive  Neural Data Privacy Rights – Rethinking ethics for neuro-sensing  Digital Art and Philosophy – Integration of science/technology, aesthetics, and meaning-making in complex human endeavor  A Critical Theory of BioArt – How artists appropriating biological materials and practices to create art is or is not art  Conceptualizing Big Data – How big data is remaking our world  Live Philosophy Workshop – Hands on concept generation  Services  Strategic Collaborations, Research Papers, Articles  Speaking engagements, Workshops, Classes, Conferences  Philosophy Studies: Epistemology1 , Subjective Experience2 78 1 http://genomera.com/studies/knowledge-generation-through-self-experimentation 2 http://genomera.com/studies/subjective-experience-citizen-qualia-study http://melanieswan.com/TFI.html
  79. 79. 7 February 2014 Université Paris Descartes, Paris France Slides: http://slideshare.net/LaBlogga Melanie Swan m@melanieswan.com Merci! Questions? Philosophy of Big Data and Quantified Self: Personal Data becomes Big Data

×