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.
Presentation on how to chat with PDF using ChatGPT code interpreter
Quantified Self Ideology: Personal Data becomes Big Data
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.
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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
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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
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QS Big Data
Agenda
Personal Data
Quantified Self
Quantified Self and Big Data
Advanced QS Concepts
Group Data
Urban Data
Conclusion
7
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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.
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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
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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
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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
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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
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QS Big Data
13
Source: http://www.meetup.com/QSParis/, http://www.meetup.com/ParisGlassUG/
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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
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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
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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.
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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
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Source: http://www.DIYgenomics.org
http://genomera.com/studies/dopamine-genes-and-rapid-reality-adaptation-in-thinking
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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.
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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.
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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
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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
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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
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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
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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
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Basis for Networked Sensing Protocols
26
Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic
Biomimicry, Synthetic Biology
Fish, Hive, Swarm
Turbulence, Chaos, Perturbation
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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
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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
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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.
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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
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QS Big Data
QS Big Data: Personal Health ‘Omics’
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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.
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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.
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APIs and Multi-QS Data Stream Integration
33
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Fluxstream Unified QS Dashboard
34
Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/
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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
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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.
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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.
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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.
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QS Big Data
Github: de facto
QS Data
Commons
39
Source: https://github.com/beaugunderson/genome
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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
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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.
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QS Big Data
Next-gen QS Services: Behavior Change
42
Source: http://askmeevery.com/
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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
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Next-gen QS Services: 3D Quantification
44
BodyMetrics and Poikos:
Fitness and Clothing
Customization Apps
OMsignal: Smart Apparel
24/7 Biometric Monitoring
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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.
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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.
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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
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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.
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QS Big Data
QS Big Data Frontier: DIYneuroscience
49
http://www.diygenomics.org/files/DIYneuroscience.pdf
https://www.facebook.com/DIYneuroscience
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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.
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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
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Group Data: Smart City, Future City
Image: http://www.sydmead.com
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QS Big Data
Global Population: Growing and Aging
54
Source: UN Habitat – 2010
http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html
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QS Big Data
3 billion new Internet users by 2020
55
Source: Peter Diamandis Singularity University
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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
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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
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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)
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QS Big Data
Urban Data: 3D Buildings + Population Density
60
Source: ViziCities
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3D Tweet Landscape, ODI Chips
61
Source: http://vimeo.com/67872925
http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl
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QS Big Data
3D Urban Data Viz: Decision-making Tool
62
Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/
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QS Big Data
Group Data: Office Building Community
63
Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010
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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
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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
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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/
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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
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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
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QS Big Data
Agenda
Personal Data
Quantified Self
Quantified Self and Big Data
Advanced QS Concepts
Group Data
Urban Data
Conclusion
70
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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
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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
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Privacy
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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
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Wearable Electronics
Urban Data
Biocitizen
Personal Data
Group Data
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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
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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
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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