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Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
Quantified Self Ideology:  Personal Data becomes Big Data
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Quantified Self Ideology: Personal Data becomes Big Data

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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 …

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.

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  • 1. Quantified Self Ideology: Personal Data becomes Big Data Melanie Swan m@melanieswan.com 7 February 2014 Université Paris Descartes, Paris France Slides: http://slideshare.net/LaBlogga
  • 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:      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. 7 February 2014 QS Big Data Source: http://melanieswan.com/publications.htm 2
  • 3. Progress of TechnoHuman Evolution 7 February 2014 QS Big Data 3
  • 4. Data Big Data! 7 February 2014 QS Big Data 4
  • 5. Human’s Role in the World is Changing 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 7 February 2014 QS Big Data 5
  • 6. Conceptualizing Big Data Categories Personal Data Tension: Individual vs Institution Open Data Group Data Sense of data belonging to a group 7 February 2014 QS Big Data 6
  • 7. Agenda  Personal Data     Group Data   Quantified Self Quantified Self and Big Data Advanced QS Concepts Urban Data Conclusion 7 February 2014 QS Big Data 7
  • 8. What is the Quantified Self?  Individual engaged in the selftracking 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 7 February 2014 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 8
  • 9. QS Sensor Mania! Wearable Electronics Increasingly continuous and automated data collection Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre), Scanaflo Urinalysis 1 Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API (Sano Intelligence), Continuous Monitors (Medtronic) 7 February 2014 QS Big Data Source: Swan, M. Sensor Mania! J Sens Actuator Netw 2012. 1 Glucose, protein, leukocytes, nitrates, blood, bilirubin, urobilinogen, specific gravity, and pH urinalysis 9
  • 10. Wearables: a Platform and an Ecosystem Smart Gadgetry Creates Continuous Personal Information Climate New Wearable Platforms: Smartwatch, AR/Glass, Contacts Smartphone PC/Tablet/Cloud 7 February 2014 QS Big Data AR = Augmented Reality 10
  • 11. Miniaturization: BioSensor Electronic Tattoos Wearable Electronics: Detect External BioChemical Threats and Track Internal Vital Signs Electrochemical Sensors Chemical Sensors 7 February 2014 QS Big Data Disposable Electronics Source: http://www.jacobsschool.ucsd.edu/pulse/winter2013/page3.shtml#tattoos Tactile Intelligence: Haptic Data Glove 11
  • 12. 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? Videos, Conferences, Meetup Groups 7 February 2014 QS Big Data Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012. 12
  • 13. 7 February 2014 QS Big Data Source: http://www.meetup.com/QSParis/, http://www.meetup.com/ParisGlassUG/ 13
  • 14. Quantified Self Project Examples  Food consumption (1 yr)1 and the Butter Mind study2 Study  Low-cost home-administered blood, urine, saliva tests  Cholestech LDX home cholesterol test 7 February 2014 QS Big Data 1 2 OrSense continuous non-invasive glucose monitoring Source: http://flowingdata.com/2011/06/29/a-year-of-food-consumption-visualized Source: http://quantifiedself.com/2011/01/results-of-the-buttermind-experiment ZRT Labs dried blood spot tests 14
  • 15. Quantified Self Measurements…  Physical Activities   Diet and Nutrition    Location, architecture, weather, noise, pollution, clutter, light, season Situational Variables   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   Calories consumed, carbs, fat, protein, specific ingredients, glycemic index, satiety, portions, supplement doses, tastiness, cost, location Psychological, Mental, and Cognitive States and Traits   Miles, steps, calories, repetitions, sets, METs1 Context, situation, gratification of situation, time of day, day of week Social Variables  7 February 2014 QS Big Data Influence, trust, charisma, karma, current role/status in the group or social network METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-thatperfect-quantified-self-app-notes-to-developers-and-qs-community-html/ 1 15
  • 16. The Quantified Self is Mainstream  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%” 7 February 2014 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 16
  • 17. QS Experimentation Motivation and Features  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 7 February 2014 QS Big Data Source: DIYgenomics Knowledge Generation through Self-Experimentation Study http://genomera.com/studies/knowledge-generation-through-self-experimentation 17
  • 18. 7 February 2014 QS Big Data Source: http://www.DIYgenomics.org http://genomera.com/studies/dopamine-genes-and-rapid-reality-adaptation-in-thinking 18
  • 19. History of the Quantified Self   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 7 February 2014 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 19
  • 20. Sensor Mania! QS Gadgetry Trend 7 February 2014 QS Big Data Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 20
  • 21. Wireless Internet-of-Things (IOT) Image credit: Cisco 7 February 2014 QS Big Data Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 21
  • 22. 6 bn Current IOT devices to double by 2016 3 year doubling cycle 7 February 2014 QS Big Data Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T 22
  • 23. 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 7 February 2014 QS Big Data Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012. http://singularitysummit.com/schedule 1 23
  • 24. 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 7 February 2014 QS Big Data Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics 24
  • 25. Networked Sensing – New Topology Unprecedented Scale Requires New Communications Protocols Machine:Machine VL Sensor Networks Internet of Things 6LoWPANS Human:Machine Machine:Machine Internet Protocol Packet Switching Human:Human Telephone System (POTS) 7 February 2014 QS Big Data 25
  • 26. Basis for Networked Sensing Protocols Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic Biomimicry, Synthetic Biology Fish, Hive, Swarm 7 February 2014 QS Big Data Turbulence, Chaos, Perturbation 26
  • 27. Defining Trend of Current Era: Big Data    Annual data creation in zettabytes (10007 bytes) 90% of the world’s data created in the last 2 years Sectors: personal, corporate, government, scientific 2 year doubling cycle 7 February 2014 QS 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 27
  • 28. 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 7 February 2014 QS Big Data http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx 28
  • 29. QS is inherently a Big Data problem   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  7 February 2014 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 29
  • 30. QS Big Data Challenge Predictive Cardiac Risk Monitoring  Heart rate monitor sampling     Cardiac events can be predicted two weeks ahead of time Phase I:     250 times per second 9 gigabytes of data per person per month Collect, store, process, analyze data Compression and search algorithms Identify event triggers Phase II  Predict and intervene with low false-positives 7 February 2014 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 30
  • 31. QS Big Data: Personal Health ‘Omics’ DNA: SNP mutations DNA: Structural variation RNA expression profiling Health 2.0: Personal Health Informatics Proteomics Microbiomics Epigenetics Metabolomics 7 February 2014 QS Big Data 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. 31
  • 32. QS Big Data: Personal Information Streams ‘Omics’ Traditional Genome: SNP mutations Structural variation Epigenetics Personal and Family Health History Microbiome Quantified Self Prescription History Transcriptome Metabolome Proteome Diseasome Environmentome Self-reported data: health, exercise, food, mood journals, etc. Lab Tests: History and Current Standardized Questionnaires Smart Home Smart Car Mobile App Data Demographic Data Internet-of-Things Personal Robotics Quantified Self Device Data Environmental Sensors Biosensor Data Objective Metrics Community Data Legend: Consumer-available 7 February 2014 QS Big Data Source: Kido, Swan, et al. Systematic evaluation of personal genome services. Nature: Journal of Human Genetics (2013) 58, 734–741. 32 32
  • 33. APIs and Multi-QS Data Stream Integration 7 February 2014 QS Big Data 33
  • 34. Fluxstream Unified QS Dashboard 7 February 2014 QS Big Data Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/ 34
  • 35. Sen.se Integrated QS Dashboard  ‘Mulitviz’ display: investigate correlation between coffee consumption, social interaction, and mood 7 February 2014 QS Big Data Source: http://blog.sen.se/post/19174708614/mashups-turning-your-data-intosomething-useable-and 35
  • 36. 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   7 February 2014 QS Big Data Source: Heianza et al. High normal HbA(1c) levels were associated with impaired insulin secretion. Diabet Med 2012. 29:1285-1290. 1 36
  • 37. 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 7 February 2014 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 37
  • 38. 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, frontend 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 7 February 2014 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 38
  • 39. Github: de facto QS Data Commons 7 February 2014 QS Big Data Source: https://github.com/beaugunderson/genome 39
  • 40. QS Frontier: Mental Performance Optimization Mood Management Apps from Mobilyze and M. Morris PTSD App ‘Siri 2.0’ Personal Virtual Coach from DIYgenomics Source: http://www.ptsd.va.gov/pu blic/pages/ptsdcoach.asp Sources: http://cbits.northwestern.edu and http://quantifiedself.com/2009/03/a-few-weeks-ago-i 7 February 2014 QS Big Data Source: DIYgenomics Social Intelligence Study http://diygenomics.pbworks.com/w/page/48946791/social_intelligence 40
  • 41. Next-gen QS Services: Quality of Life QS Aspiration Apps: Happiness, Emotive State (personal and group), Well-being, Goal Achievement 7 February 2014 QS Big Data 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. 41
  • 42. Next-gen QS Services: Behavior Change 7 February 2014 QS Big Data Source: http://askmeevery.com/ 42
  • 43. 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 7 February 2014 QS Big Data Source: http://mtmr.jp/en/papers/taai2013v2.pdf 43
  • 44. Next-gen QS Services: 3D Quantification BodyMetrics and Poikos: Fitness and Clothing Customization Apps OMsignal: Smart Apparel 24/7 Biometric Monitoring 7 February 2014 QS Big Data 44
  • 45. Subjectivation: The TechnoBioCitizen   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 7 February 2014 QS Big Data Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 45
  • 46. Building Exosenses for the Qualified Self Extending our senses in new ways to perceive data as sensation 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 7 February 2014 QS Big Data Serendipitous Joy: Smiletriggered EMG muscle sensor with an LED headband display Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 46
  • 47. 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 7 February 2014 QS Big Data Source: web.mit.edu/newsoffice/2012/human-body-on-a-chip-research-funding-0724.html Nose-on-a-chip Gut-on-a-chip Lung-on-a-chip Chip-on-a-Ring 47
  • 48. QS Big Data Frontier: Neural Tracking 24/7 Consumer EEG, Eye-tracking, Emotion-Mapping, Augmented Reality Glasses Consumer EEG Rigs Augmented Reality Glasses 1.0 2.0 7 February 2014 QS Big Data Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 48
  • 49. QS Big Data Frontier: DIYneuroscience 7 February 2014 QS Big Data http://www.diygenomics.org/files/DIYneuroscience.pdf https://www.facebook.com/DIYneuroscience 49
  • 50. QS Big Data: Biocitizen is Locus of Action 1. Continuous health information climate Automated digital health monitoring, self-tracking devices, and mobile apps providing personalized recommendations 2. Peer collaboration and health advisors Individual 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 7 February 2014 QS Big Data 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. 50
  • 51. Conceptualizing Big Data Categories Personal Data Tension: Individual vs Institution Open Data Group Data Sense of data belonging to a group 7 February 2014 QS Big Data 51
  • 52. Agenda  Personal Data     Group Data   Quantified Self Quantified Self and Big Data Advanced QS Concepts Urban Data Conclusion 7 February 2014 QS Big Data 52
  • 53. Group Data: Smart City, Future City 7 February 2014 QS Big Data Image: http://www.sydmead.com 53
  • 54. Global Population: Growing and Aging 7 February 2014 QS Big Data Source: UN Habitat – 2010 http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html 54
  • 55. 3 billion new Internet users by 2020 7 February 2014 QS Big Data Source: Peter Diamandis Singularity University 55
  • 56. Human Urbanization: Living in Cities    Over 50% worldwide population in 2008 5 billion in 2030 (estimated) Megacity: (>10 million and possibly 2,000/km 2) 7 February 2014 QS Big Data 56
  • 57. Megacity Growth Rates 7 February 2014 QS Big Data Source: Wikipedia 57
  • 58. Big Urban Data: Killer Apps  Public transit, traffic management, eTolls, parking, adaptive lighting, smart waste, pest control, hygiene management, asset tracking, smart power grid 7 February 2014 QS Big Data Source: Copenhagen Pollution Levels, MIT Senseable City Lab 58
  • 59. Data Signature of Humanity MIT SENSEable City Lab – the Real-Time City  Flexible services responding predictively to individual and community-level demand (ex: pedestrian load) 7 February 2014 QS Big Data Source: http://senseable.mit.edu/signature-of-humanity/ 59
  • 60. Urban Data: 3D Buildings + Population Density 7 February 2014 QS Big Data Source: ViziCities 60
  • 61. 3D Tweet Landscape, ODI Chips 7 February 2014 QS Big Data Source: http://vimeo.com/67872925 http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl 61
  • 62. 3D Urban Data Viz: Decision-making Tool 7 February 2014 QS Big Data Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/ 62
  • 63. Group Data: Office Building Community 7 February 2014 QS Big Data Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010 63
  • 64. Big Data 3D Printed Dwellings of the Future Living Treehouses – Mitchell Joachim Masdar, Abu Dhabi – Energy City of the Future 7 February 2014 QS Big Data Himalayas Water Tower
  • 65. Agricultural Innovation: Vertical Farms, Tissue-Engineered Meat Modern Meadow (existing)1 San Diego, California (planned) 7 February 2014 QS Big Data Singapore (existing) 1 Source: http://www.popsci.com/article/science/can-artificial-meat-save-world 65
  • 66. Reconfiguration of Space: Seasteading 7 February 2014 QS Big Data 66
  • 67. Transportation Revolution Solar Power: Tesla + Solar City Personalized Pod Transport 7 February 2014 QS Big Data Self-Driving Car 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/ 67
  • 68. Another Pervasive Trend: Crowdsourcing 7 February 2014 QS Big Data Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw 68
  • 69. 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 7 February 2014 QS Big Data 69
  • 70. Agenda  Personal Data     Group Data   Quantified Self Quantified Self and Big Data Advanced QS Concepts Urban Data Conclusion 7 February 2014 QS Big Data 70
  • 71. But wait…Limitations and Risks   Transition to access not ownership models Data rights and responsibilities   Regulatory and policy tensions      Personal data and group data 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 7 February 2014 QS Big Data 71
  • 72. Evolving Shape of #1 Concern: Privacy  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 Privacy 7 February 2014 QS Big Data 72
  • 73. Proliferation of New QS Big Data Flows  QS Device Data     Personal IOT Data    Cell phone, wearable electronics data Smartphone digital identity & payment Personal Urban Data    Biometric data (HRM), personal genomic data Personal medical and health data QS neural-tracking, eye-tracking, affect data Smart home, smart car Smart city data (e.g.; transportation) Personal Robotics Data 7 February 2014 QS Big Data 73
  • 74. Top 10 QS Big Data Trends Personal Data QS Device Ecosystem Group Data Wearable Electronics Internet-of-Things (IOT) 3D Information Sensor Networks Visualization Smart City Future City Megacity Growth Urban Data 7 February 2014 QS Big Data 3 billion New People Online Biocitizen Crowdsourcing Self-Empowerment DIY Attitude 74
  • 75. QS Big Data Summary  Next-gen QS services     IOT continuous personal information climates QS Big Data    Wholly different relation to data: 99% noise Rights and responsibilities model of data access Group Data   Wearable Electronics as the QS platform Improve quality of life, facilitate behavior change Megacity growth, urban data flow, 3 bn coming online Personal Data  Technology-enabled biocitizen self-produces in the data environment and takes action 7 February 2014 QS Big Data 75
  • 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 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 7 February 2014 QS Big Data Source: Heidegger, M. The Question Concerning Technology, 1954; Derrida, J. Paper Machine, 2005 76
  • 77. Technology Futures Institute http://melanieswan.com/TFI.html  Apply philosophical principles to modern technology Ontology Existence Synthetic Biology Meaning-making Subjectivation Reality Aesthetics Valorization Ethics Language 7 February 2014 QS Big Data Big Data Cognitive Enhancement Biohacking 3D Printing Wearables Bioart NanoCognition Surveillance Society http://melanieswan.com/TFI.html 77
  • 78. 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 7 February 2014 QS Big Data http://melanieswan.com/TFI.html http://genomera.com/studies/knowledge-generation-through-self-experimentation 2 http://genomera.com/studies/subjective-experience-citizen-qualia-study 1 78
  • 79. Quantified Self Ideology: Personal Data becomes Big Data Merci! Questions? 7 February 2014 Université Paris Descartes, Paris France Slides: http://slideshare.net/LaBlogga Melanie Swan m@melanieswan.com

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