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. 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. 7 February 2014
QS Big Data
Progress of TechnoHuman Evolution
3

4. 7 February 2014
QS Big Data 4
Data
Big Data!

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. 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 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. 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. 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. 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. 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. 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. 7 February 2014
QS Big Data
13
Source: http://www.meetup.com/QSParis/, http://www.meetup.com/ParisGlassUG/

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 7 February 2014
QS Big Data
APIs and Multi-QS Data Stream Integration
33

34. 7 February 2014
QS Big Data
Fluxstream Unified QS Dashboard
34
Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/

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. 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. 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. 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. 7 February 2014
QS Big Data
Github: de facto
QS Data
Commons
39
Source: https://github.com/beaugunderson/genome

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. 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. 7 February 2014
QS Big Data
Next-gen QS Services: Behavior Change
42
Source: http://askmeevery.com/

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 7 February 2014
QS Big Data 53
Group Data: Smart City, Future City
Image: http://www.sydmead.com

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. 7 February 2014
QS Big Data
3 billion new Internet users by 2020
55
Source: Peter Diamandis Singularity University

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. 7 February 2014
QS Big Data 57
Megacity
Growth
Rates
Source: Wikipedia

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. 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. 7 February 2014
QS Big Data
Urban Data: 3D Buildings + Population Density
60
Source: ViziCities

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. 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. 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. 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. 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. 7 February 2014
QS Big Data
Reconfiguration of Space: Seasteading
66

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. 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. 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
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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. 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. 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. 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
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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. 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
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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. 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. 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. 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