Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web

1
Computing for Human Experience
Keynote at On-the-Move Federated Conference, October 2011:
http://www.onthemove-conferences.org/
Amit Sheth
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, OH, USA
Special thanks & contributions: Cory Henson

2
with 7 billion people
Our 1 world
living 1 experience at a time

3
with a stream of experiences to be …
shared

4
Today, technology increasingly engages individuals,
society and humanity with …
5 billion
mobile phones
40+ billion
mobile sensors
1-2 billion
computers
http://www.gartner.com/it/page.jsp?id=703807

5
With constant connectivity enabled through global networks
5 billion
mobile phones
40+ billion
mobile sensors
1-2 billion
computers

6
So, how can we leverage this tech to improve our experiences
without losing ourselves in the process?

8
From machine-centric to human-centric design
Machines to accommodate our experiences,
as opposed to the other way around
Computing to liberate

9
To accomplish this requires a fundamental
shift in how we interact and communicate
with computational machines
We must take a more holistic view of computation,
as a shared universe, populated by people and machines
working in harmony to achieve our highest aspirations

10
We have caught glimpses of this vision …
Man-Machine Symbiosis – T. O’Reilly
Memex – V. Bush
Ambient Intelligence – E. Zelkha, B. Epstein
Ubiquitous Computing – M. Weiser

11
Let this affect be positive
The ways in which technology and humans
interact are fundamentally changing
In turn, our (human) experiences are changing
Our activities, decisions, thoughts, and feelings
are affected by the ubiquitous integration of
technology into the fabric of our lives

12
Amit Sheth
Wright State University, Dayton, OH, USA
Special thanks & contributions: Cory Henson

Amit Sheth
Ashutosh Jadhav
Hemant
Purohit
Vinh
Nguyen
Michael
Cooney
Lu Chen
Pavan
Kapanipathi
Pramod
Anantharam
Sujan
Perera
Alan Smith
Pramod Koneru
Maryam Panahiazar
Sarasi Lalithsena
Prateek
Jain
Matthan Sink
Cory Henson
Ajith
Ranabahu
Kalpa
Gunaratna
Delroy
Cameron
Sanjaya
Wijeratne
Wenbo
Wang

14
Human capabilities such as
sensing, perception, attention, memory,
decision making, control, etc.
CHE is an approach to improving the human
condition through computational means, and
with minimal burden
This may be achieved through the contextual assistance,
augmentation, and absolution of human capabilities

15
Consider the following example …

A cross-country flight from New York to Los Angeles on a
Boeing 737 plane generates a massive 240 terabytes of data
- GigaOmni Media
16

17
But, how much data is generated regarding the
health and well-being of the pilot or passengers?
zero, none, zilch!

18
Image the ability to monitor and control our health
with the same care and precision that goes into the 737.
And not just providing
doctors with such control,
but you and me.

19
Health information is now available from multiple sources
•  medical records
•  background knowledge
•  social networks
•  personal observations
•  sensors
•  etc.

20
Sensors, actuators, and mobile computing are playing an
increasingly important role in providing data for early phases of
the health-care life-cycle
This represents a fundamental shift:
•  people are now empowered to monitor and manage their own health;
•  and doctors are given access to more data about their patients

21
Unfortunately, when personal health data is collected
and presented, it often looks like this … gibberish.

22
Health Metrics with Meaning
Personal Health Dashboard
What is needed is a more intuitive and
intelligent representation of our health.
Image: http://bit.ly/lV2V73

24
•  Integration of heterogeneous, multimodal data
•  Bridging the physical-cyber-social divide
•  Elevating abstractions that machines and people understand
•  Semantics at an extraordinary Scale
These enablers are brought together through
Semantic Web technologies
Key Enablers of CHE

25
Foundation on which these enablers stand

26
Foundation on which these enablers stand

27
Integration of heterogeneous, multimodal data

28
Background Knowledge: ontologies, knowledge bases, LOD,
databases, etc.
Social/Community Data: social network data, wisdom of the
crowds, etc.
Sensor Data: observations from machine sensors, citizen sensors (i.e.,
patients, doctors), laboratory experiments, etc.
Personal Context: location, schedule, items (e.g., accessible sensors),
etc.
Personal Medical History: Electronic Medical Records, Personal
Health Records, Patient Visit Records, etc.
Integration of heterogeneous, multimodal data

29
Communications using online technologies
to share opinions, insights, experiences and
perspectives with each other.
What is Social Media?

30
Blogs – DiabetesMine, HealthMatters, WebMD, NYT HealthBlog, etc.
Microblogs – Livestrong, Stupid Cancer, etc.
Social Networks – OrganizedWisdom, PatientsLikeMe, DailyStrength,
NursesRecommendDoctors, CureTogether, etc.
Podcasts – John Hopkins Medical Podcasts, Mayo Clinic, etc.
Forums – Revolution Health Groups, Google Health Groups, etc.
Popular types of Healthcare Social Media

31
HCPs aren’t waiting to be detailed, they’re turning to the
social web to educate themselves
60% of physicians either use or are interested in using social networks
65% of docs plan to use
social media for
professional development
Manhattan Research 2009, 2010
Sermo,com
Compete.com
This doc-to-doc
blogger has
53,000 readers
this month +
20,000 Twitter
followers
112,000 docs
talk to each
other on Sermo.
http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462
60% of physicians either use or are interested in using social networks

32 http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462
People are turning to each other online to understand their health

33
83% of online adults search for health information

34
83% of online adults search for health information
60% of them look for the experience of “someone like me”

35
"I don't know, but I can try to find out" is the
default setting for people with health questions.
Savannah Fox, The Social Life of Health Information, Pew Internet Report, May 12, 2011.
Available at http://www.pewinternet.org/Reports/2011/Social-Life-of-Health-Info.aspx
"I know, and I want to share my knowledge"
is the leading edge of health care.

36
Intra Community Activity and connectivity
–  How well connected are individual nodes (People)
–  What keeps them strongly connected over time
(Relationship types - Knowledge of Content)
Inter-Community Connectivity
•  Any bridges to connect to the other community?
(People)
•  Any Similarity in actions with the other community
(Can Content help?)Image: http://themelis-cuiper.com
Will the two communities coordinate well
during an event- crisis or disaster?
• Interplay between all three dimensions –
P, C, N
People-Content-Network Analysis
For more info: http://www.slideshare.net/knoesis/understanding-usercommunity-engagement-
by-multifaceted-features-a-case-study-on-twitter

37
People-Content-Network Analysis
External
Knowledge bases
Dynamic
Domain
Model
for the
event
Event oriented
Community
Social Network
Mined User Interests
and User Types
User
Profiles
SEMANTIC
ASSOCIATION TO
UNDERSTAND
ENHANCED
ENGAGEMENT LEVEL

38
Bridging the physical-cyber-social divide
Computation is no longer confined to pure symbol manipulation. The previously strict
relations between the digital and physical world are blurring.

39
Psyleron’s Mind-Lamp (Princeton U),
connections between the mind and the
physical world.
Neuro Sky's mind-controlled headset to
play a video game.
MIT’s Fluid Interface Group: wearable
device with a projector for deep
interactions with the environment
Bridging the physical-cyber divide

40
Foursquare is an online application which
integrates a persons physical location and
social network.
Community of enthusiasts that share experiences of
self-tracking and measurement.
FitBit Community allows the
automated collection and
sharing of health-related data,
goals, and achievements

41
Tweeting Sensors
sensors are becoming social

42
Select topicSelect date
Topic tree
Spatial Marker
N-gram summaries
Wikipedia articles
Reference newsRelated tweets
Images & Videos
Tweet traffic
Sentiment
Analysis
Network Analysis
Community
Interaction of
Various user types
For more info: http://twitris.knoesis.org/

43
Heliopolis is a
suburb of Cairo.
Dynamic Model Creation
Continuous Semantics

44
Events
“Both Ahmadinejad & Mousavi
declare victory in Iranian
Elections.”
“situation in tehran University is
so worrisome. police have
attacked to girls dormitory #tehran
#iranelection”
“Reports from Azadi Square - 4
people killed by police, people
killed police who shot. More shots
being fired #iranelections”
June 12 2009 June 13 2009 June 15 2009
KeyphrasesModels
Ahmadinejad &
Mousavi are
politicians in Iran
Tehran University
is a University in
Iran
Azadi Square is a
city square in
Tehran
Dynamic Model Creation

45
The design and building of physical-cyber-social systems requires effective
conceptualization and communication between people and machines.
To reach this vision requires advancement in the area of machine perception,
enabling machines the ability to abstract over low-level observations.

46
Abstraction
Abstraction provides the ability to interpret and synthesize information in a way that
affords effective understanding and communication of ideas, feelings, perceptions, etc.
between machines and people.

47
The process of interpreting stimuli is called perception;
and studying this extraordinary human capability can lead
to insights for developing effective machine perception.
People are excellent at abstraction; of
sensing and interpreting stimuli to
understand and interact with the world.

“real-world”
conceptualization of
“real-world”
Sensor
Sensor Data /
Social Data
observation
perception
Physical
Cyber
Social
48

49
Both people and machines are capable of observing qualities, such as redness.
* Formally described in a sensor/observation ontology
observes
Observer Quality

50
Sensor and Sensor Network (SSN) Ontology
http://www.w3.org/2005/Incubator/ssn/XGR-ssn/

51
The ability to perceive is afforded through the use of background
knowledge, relating observable qualities to entities in the world.
* Formally described in
domain ontologies
(and knowledge bases)
inheres in
Quality
Entity

52
http://linkedsensordata.com

53
With the help of sophisticated inference, both people and machines are
also capable of perceiving entities, such as apples.
• the ability to degrade gracefully with incomplete information
• the ability to minimize explanations based on new information
• the ability to reason over data on the Web
• fast (tractable)
perceives
EntityPerceiver

54
minimize
explanations
degrade gracefully
tractable
Web reasoning
Abductive Logic
high complexity
Deductive Logic (e.g., OWL)
(relatively) low complexity
Perceptual Inference
(i.e., abstraction)
Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering
Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED
2011), San Francisco, CA, USA, June 5-6, 2011.

55
•  Goal is to account for observed symptoms
with plausible explanatory hypotheses
(abductive logic)
•  Driven by background knowledge modeled
as a bipartite graph causally linking
disorders to manifestations
Yun Peng, James A. Reggia, "Abductive Inference Models for Diagnostic Problem-Solving"
m1
m2
m3
d1
d2
d3
m4
disorder manifestationcauses
explanation
observations
Parsimonious Covering Theory

56
PCT Parsimonious Cover
•  coverage: an explanation is a cover if, for each observation, there is
a causal relation from a disorder contained in the explanation to the
observation
•  parsimony: an explanation is parsimonious, or best, if it matches
some criteria of suitability (i.e., single disorder assumption)

57
Given
PCT problem P is a 4-tuple ⟨D, M, C, Γ⟩
•  D is a finite set of disorders
•  M is a finite set of manifestations
•  C is the causation function [C : D ⟶ Powerset(M)]
•  Γ is the set of observations [Γ ⊆ M ]
à Δ is a valid explanation (i.e., is a parsimonious cover)
Goal
Translate P into OWL, o(P), such that o(P) ⊧ Δ
Convert PCT to OWL

58
headache
extreme exhaustion
severe ache and pain
stuffy nose
sneezing
sore throat
severe cough
mild ache and pain
mild cough
flu
cold
fever
disorder manifestationcauses
PCT Background
Knowledge in OWL
disorders (D)
for all d ∈ D, write d rdf:type Disorder
ex: flu rdf:type Disorder
cold rdf:type Disorder
manifestations (M)
for all m ∈ M, write m rdf:type Manifestation
ex: fever rdf:type Manifestation
headache rdf:type Manifestation …
causes relations (C)
for all (d, m) ∈ C, write d causes m
ex: flu causes fever
flu causes headache …

59
observations (Γ)
for mi ∈ Γ, i =1 … n, write
Explanation owl:equivalentClass
causes value m1 and … causes value mn
ex: Explanation owl:equivalentClass
causes value sneezing and
causes value sore-throat
causes value mild-cough
explanation (Δ)
Δ rdf:type Explanation, is deduced
ex: cold rdf:type Explanation
flu rdf:type Explanation
and
PCT Observations and
Explanations in OWL

60
The ability to perceive efficiently is afforded through the cyclical
exchange of information between observers and perceivers.
Traditionally called the
Perception Cycle
(or Active Perception)
sends
focus
sends
observation
Observer
Perceiver

61
Nessier’s Perception Cycle

62
Cognitive Theories of Perception (timeline)
1970’s – Perception is an active, cyclical process of exploration and
interpretation. - Nessier’s Perception Cycle
1980’s – The perception cycle is driven by background knowledge in
order to generate and test hypotheses. - Richard Gregory (optical illusions)
1990’s – In order to effectively test hypotheses, some observations are
more informative than others. - Norwich’s Entropy Theory of Perception

63
Key Insights
•  Background knowledge plays a crucial role in perception; what we know (or
think we know/believe) influences our perception of the world.
•  Semantics will allow us to realize computational models of perception based
on background knowledge.
•  Internet/Web expands our background knowledge to a global scope; thus
our perception is global in scope
•  Social networks influence our knowledge and beliefs, thus influencing our
perception
Contemporary Issues

64
observes
inheres in
Integrated together, we have an general model – capable of abstraction –
relating observers, perceivers, and background knowledge.
perceives
sends
focus
sends
observation
Observer Quality
EntityPerceiver

65
Modeled in set-theoretic notation with components
mapped to Parsimonious Covering Theory and OWL
Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing
Machine Perception on the Web. Applied Ontology, 2011 (accepted).

66
Applications of
HealthcareWeather Rescue

67
Weather Application
Detection of events, such as blizzards, from
weather station observations on LinkedSensorData
Weather Application
Demos: Real-Time Feature Streams

68
Weather ApplicationSECURE: Semantics Empowered Rescue Environment
Rescue robots detect different types of fires, which may require different
methods/tools to extinguish, and relays this knowledge to first responders.
Demo: SECURE: Semantics Empowered Rescue Environment

69
Weather ApplicationHealthcare Application
Detection of errors in Electronic Medical Records and
missing knowledge in a cardiology domain model
EMR

70
EMR: "Her prognosis is poor both short term and long term, however, we
will do everything possible to keep her alive and battle this infection."
SNM:40733004_infection SNM:68566005_infection_urinary_tract
A syntax based NLP extractor
(such as Medlee) can extract
this term and annotate as
SNM:40733004_infection
By utilizing IntellegO and cardiology
background knowledge, we can more
accurately annotate the term as SNM:
68566005_infection_urinary_tract
without IntellegO
with IntellegO
Problem Problem

71
EMR: ”The patient is to receive 2 fluid buloses."
SNM:32457005_body_fluid
A syntax based NLP extractor
(such as Medlee) can extract
this term and annotate as
SNM:32457005_body_fluid
without IntellegO
Problem
Fluid is part of buloses treatment, not a problem
with IntellegO
By utilizing IntellegO and cardiology
background knowledge, we can determine
that this is an incorrect annotation.
Treatment

72
In 2008, the rate of data generation surpassed storage capacity. With 7 billion people, and a
growing number of sensors, how can such a such a system scale? By shining a light on
relevant human experience, supported by knowledge, while dimming the minutia of data.
Semantic Scalability
http://gigaom.com/cloud/sensor-networks-top-social-networks-for-big-data-2

73
ex. – keyword based search/index, non-textual data, multimodal data for an event
It is clear that purely syntax-based solutions will not scale

74
1.  Focusing attention on important information and ignoring irrelevant data
2.  Converting low-level data (observations) to high-level knowledge (abstractions)
3.  Utilizing CHE technology to more evenly distribute responsibility and activities
among people and machines
Path to Web scale semantics

75
We were able to demonstrate 50% savings in sensing
resource requirements during the detection of a blizzard.
1. Focusing attention on important
information and ignoring irrelevant data
Machine Perception on the Web. Applied Ontology, 2012. (accepted)

76
2. Converting low-level data to
high-level knowledge
(observations to abstractions)
Experiment – during a blizzard, we utilized Intelleg0 to collect
and analyze over 110,000 sensor observations, from:
•  800 weather stations (~5 sensors per station)
•  across 5 states (Utah, Nevada, Colorado, Wyoming, and Idaho)
•  for 6 days (April 1 – 6, 2003)

77
We were able to demonstrate an order of magnitude resource
savings between storing observations vs. relevant abstractions

78
While this is a good result, the benefit provided for a single
person – a single experience – is far more dramatic.

There are almost 7 billion people on earth, and only ~10-15 million doctors (~700:1 - 467:1)
3. Utilizing CHE technology to more evenly distribute
responsibility and activities among people and machines
79

80
Providing people with the tools to monitor and manage their own health will
dramatically reduce the burden on doctors, and improve the health of the people
These doctors are severely overburdened,
answering less than 60% of questions posed by
patients regarding their health and well being

81
The health-care ecosystem of the future includes
machines, people, and social networks
continuously, ubiquitously, and unobtrusively
monitoring and managing our health

82
CHE approach to Health Care
1 à 2 à 3
Continuous Monitoring Personal Assessment Medical Service
Auxiliary Information – background knowledge, social/community support,
personal context, personal medical history

83
Continuous Monitoring Phase
Monitoring health metrics and vital signs utilizing unobtrusive body sensors
Continuously collecting information, watching for worrisome symptoms

84
Personal Assessment Phase
Assessment of symptoms from personal observation and/or health sensors
available at home.
Utilizing background knowledge, personal medical history, and current
sensor data to formulate and ask specific questions of patient that will aid in
explaining symptoms.

85
Medical Services Phase
Assessment of symptoms gathered from continuous and personal phases,
with additional sophisticated equipment, advanced treatment, and
specialized medical knowledge not previously available.
Utilizing background knowledge, personal medical history, and current
sensor data to formulate a diagnosis.

86
Continuous Personal Medical
Personal Medical History
(e.g., Electronic Medical Record,
genomic sequence)
Background Knowledge
(e.g., Ontologies,
Knowledgebases)
Auxiliary Information
Symptoms/
Explanations
Symptoms/
Explanations
access &
update (PHR)
access &
update (PHR)
access &
update (EMR)
Personal Context
(e.g., available sensors,
location, schedule)
Social/Community Support
(e.g., Patient Network, crowd
sourcing)

87
Continuous Monitoring Phase: Example
•  Abnormal heart rate
•  Clammy skin
•  Panic Disorder
•  Hypoglycemia
•  Hyperthyroidism
•  Heart Attack
•  Septic Shock
•  Check phone for instructions•  Patient has history of Heart Disease
Observed Symptoms Possible Explanations
Electronic Medical Record Health Alert

88
Basis is a wrist-watch that also monitors pulse rate, movement, temperature, and
galvanic skin response.
Continuous Phase Technology

89
Fitbit Tracker uses a MEMS 3-axis accelerometer that measures your motion
patterns to tell you your calories burned, steps taken, distance traveled, and sleep
quality.
Continuous Phase Technology

90
Personal Assessment Phase: Example
Are you feeling lightheaded?
Are you have trouble taking deep breaths?
yes
yes
1.  Take medication: Methimazole
2.  See doctor: how about Tues. @ 11am?
•  Patient has history of Hyperthyroidism
•  Patient has prescription for Methimazole
Have you taken your Methimazole
medication?
Do you have low blood pressure when
standing?
yes
•  Clammy skin
•  Lightheaded
•  Trouble breathing
•  Low blood pressure
•  Panic Disorder
•  Hypoglycemia
•  Heart Attack
•  Septic Shock
Electronic Medical Record Health Alert
no

91
Lark is a sleep sensor that monitors circadian rhythms and functions as an "un-
alarm," vibrating to wake you at a point in your sleep cycle when you feel alert, not
groggy.
Personal Phase Technology

92
Instant Heart Rate takes your pulse when you place your finger over your phone’s
camera lens. The app uses light from the camera flash to detect color changes caused
by blood moving through your finger.

93
Telcare makes a blood glucose meter (right) for diabetics that broadcasts readings to
a mobile-phone app (center) where patients can see results and set goals.

94
iBGStar is a plug-in glucose meter for the iPhone, developed by Sanofi-Aventis,
providing the ability for patients to monitor and manage Diabetes.

95
Withings Blood Pressure Monitor provides easy and convenient blood pressure
readings in the convenience of home.

96
WebMD provides a wealth of health information and an application to diagnose
symptoms.

97
Medical Services Phase: Example
•  Patient has history of Hyperthyroidism
•  Patient has prescription for Methimazole
Are your blood sugar levels low?
•  Clammy skin
•  Lightheaded
•  Trouble breathing
•  Low blood pressure
•  Hypoglycemia
Electronic Medical Record

98
Doctor.
Medical Phase Technology

99
Health Guard provides a secure way to store and analyze health records for casual
browsing or emergency use (i.e., MS Health Vault records).

100
Mobile MIM gives physicians a sophisticated, hands-on mobile system for viewing
and annotating radiology images, such as CT scans.

101
Dr. Watson is a health and medical question and answering system developed by
IBM, utilizing supercomputer intelligence for medical diagnostics.

102
Continuous Personal Medical
Personal Medical History
(e.g., Electronic Medical Record,
genomic sequence)
Background Knowledge
(e.g., Ontologies,
Knowledgebases)
Auxiliary Information
Symptoms/
Explanations
Symptoms/
Explanations
access &
update (PHR)
access &
update (PHR)
access &
update (EMR)
Personal Context
(e.g., available sensors,
location, schedule)
Social/Community Support
(e.g., Patient Network, crowd
sourcing)

103
Improving the experience of health-care
improves all other experiences
CHE holds the potential to revolutionize the practice
of health-care by embracing the relationship between
ourselves, our machines, and our health

104
“The most profound technologies are those that disappear.
They weave themselves into the fabric of everyday life until
they are indistinguishable from it.” – M. Weiser
Physical-Cyber-Social
Abstraction
Integration Scalability

105
thank you, and please visit us at
http://knoesis.org
Wright State University, Dayton, Ohio, USA
More: Vision Paper: Computing for Human Experience:
http://wiki.knoesis.org/index.php/Computing_For_Human_Experience

Image credits:

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