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Transcript of Smart Data - How you and I will exploit Big Data for personalized digital health and many other...
Smart Data - How you and I will exploit Big Data for personalized digital health and many other activities
Put Knoesis Banner
Keynote at IEEE BigData 2014, Oct 28, 2014
Amit ShethLexisNexis Ohio Eminent Scholar & Exec. Director,
The Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)Wright State, USA
2
Thanks: My team (missing Pramod, Hemant, ...)
Collaborators: Clinicians: Dr. William Abrahams (OSU-Wexner), Dr. Shalini Forbis (Dayton Childrens), Dr. Sangeeta Agrawal (VA), Valerie Shalin (WSU Cognitive Scientists ), Payam Barnaghi (U-Surrey), Ramesh Jain(UCI), …Funding: NSF (esp. IIS-1111183 “SoCS: Social Media Enhanced Organizational Sensemaking in Emergency Response,”), AFRL, NIH, Industry….
3http://hrboss.com/hiringboss/articles/big-data-infographic
Big Data 2014
4
Only 0.5% to 1% of the data is used for analysis.
http://www.csc.com/insights/flxwd/78931-big_data_growth_just_beginning_to_explodehttp://www.guardian.co.uk/news/datablog/2012/dec/19/big-data-study-digital-universe-global-volume
5
Variety – not just structure but modality: multimodal, multisensory
Structured
Unstructured
Semi structured
Audio
Video
Images
6
Velocity
Fast Data
Rapid Changes
Real-Time/Stream Analysis
Current application examples: financial services, stock brokerage, weather tracking, movies/entertainment and online retail
7
About 2 billion of the 5+ billion have data connections – so they perform “citizen sensing”.And there are more devices connected to the Internet than the entire human population.
These ~2 billion citizen sensors and 10 billion devices & objects connected to the Internet makes this an era of IoT (Internet of Things) and Internet of Everything (IoE).
http://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf
Ever Increasing Connected Devices and People
8
“The next wave of dramatic Internet growth will come through the confluence of people, process, data, and things — the Internet of Everything (IoE).”
- CISCO IBSG, 2013
http://www.cisco.com/web/about/ac79/docs/innov/IoE_Economy.pdf
Beyond the IoE based infrastructure, it is the possibility of developing applications that spansPhysical, Cyber and the Social Worlds that is very exciting.
Internet of Things / Everything : Future Trends
10
We are still working on the simpler representations of the real-world!
What has not changed?
http://artint.info/html/ArtInt_8.html http://en.wikipedia.org/wiki/Traffic_congestion
solve
represent interpret real-world
simplified representation
compute
11
What should change?
We need computational paradigms to tap into the rich pulse of the human populace,
and utilize diverse data
Represent, capture, and compute with richer and fine-grained representations of real-world
problems
solve
represent interpret real-world
richer representation
compute
+
Richer representation of traffic observations
Effective solutions
People interpreting a real-world event
High CO
High Wheeze
Reduced CO level =>
better Asthma control
Horizontal Operators(Semantic Integration) operates on data from heterogeneous sources to create Integrated/correlated data streams.
Vertical Operators(Semantic abstraction) operates onArtifacts at each level and transcends them to the next level.
Carbon Monoxide
High Luminosity
WheezeLuminosity
Low Wheeze
Low Luminosity
High CO influences Wheezing Level (Low/High)
Physical-Cyber-Social Computing for Actionable Insights from Multimodal Data
1Amit Sheth, Pramod Anantharam, Cory Henson, 'Physical-Cyber-Social Computing: An Early 21st Century Approach,' IEEE Intelligent Systems, vol. 28, no. 1, pp. 78-82, Jan.-Feb., 2013. http://doi.ieeecomputersociety.org/10.1109/MIS.2013.20
“a holistic treatment of data, information, and knowledge
from the PCS worlds to integrate, correlate, interpret,
and provide contextually relevant abstractions
to humans. ”1
12
13
• Healthcare: ADFH, Asthma, GI, Demintia– Using kHealth system
• Traffic Analytics:– Understanding traffic flow
• Social Media Analysis :– Crisis coordination using Twitris
I will use applications in 3 domains to demonstrate
14http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/
MIT Technology Review, 2012
The Patient of the Future
15
Asthma: A Multi-faceted and Symptomatically Variable Health Challenge
Personal level Signals
Public level Signals
Population level Signals
1Marcus, Philip, Kevin R. Murphy, Abid Rahman, and Christopher D. O’Brien. "Intrapatient symptom variability in adults and children with asthma: Results of a survey." Advances in therapy 22, no. 5 (2005): 488-497.
“ … survey indicates that adult patients and caregivers of pediatric patients report variability in asthma symptoms over time, even when asthma medications are taken.”1
16
Asthma: Actionable Information
How is my Asthma control?
Should I take additional medication today?
How can I reduce my asthma attacks at home?
Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise.
-- John Tukey, Ann. Math. Stat. 33 (1962)
17
Personal level Signals
Public level Signals
Population level Signals
Domain Knowledge
Asthma: Challenges in Heterogeneity, Variability, and Personalization
http://www.tuberktoraks.org/managete/fu_folder/2011-03/html/2011-3-291-311.html
Contextual Personalized Actionable
OR
18
My 2004-2005 formulation of SMART DATA - Semagix
Formulation of Smart Data strategy providing services for Search, Explore, Notify.
“Use of Ontologies and Data repositories to gain
relevant insights”
19
Smart Data (2014 retake)
Smart data makes sense out of Big data
It provides value from harnessing the challenges posed by volume, velocity, variety
and veracity of big data, in-turn providing actionable information and improve decision
making.
20
OF human, BY human FOR human
Smart data is about extracting value by improving human involvement in data creation,
processing and consumption. It is about (improving)
computing for human experience.
Another perspective on Smart Data
21Petabytes of Physical(sensory)-Cyber-Social Data everyday! More on PCS Computing: http://wiki.knoesis.org/index.php/PCS
‘OF human’ : Relevant Real-time Data Streams for Human Experience
Use of Prior Human-created Knowledge Models
22
‘BY human’: Involving Crowd Intelligence in data processing
Crowdsourcing and Domain-expert guided Machine Learning Modeling
23
Detection of events, such as wheezing sound, indoor temperature, humidity,
dust, and CO level
Weather ApplicationAsthma Healthcare
Application
High CO content at home during day
‘FOR human’ : Improving Human Experience (Smart Health)
Population Level
Personal
Public Health
Action in the Physical World
Luminosity CO level
CO in gush during day time
Electricity usage over a day, device at work, power consumption, cost/kWh,
heat index, relative humidity, and public events from social stream
Weather ApplicationPower Monitoring Application
24
‘FOR human’ : Improving Human Experience (Smart Energy)
Population Level Observations
Personal Level Observations
Action in the Physical World
Washing and drying has resulted in significant cost
since it was done during peak load period. Consider
changing this time to night.
25
Big Data is pervasive - It is Smart Data that matter!
DATAObservations from machine and social sensors
KNOWLEDGEfor interpretation of
observations
26
ACTIONSsituation awareness useful
for decision making
Primary challenge is to bridge the gap between data and actions
Contextualization
Personalization
27
In the process, engaging both top and bottom brain
“The Theory of Cognitive Modes* emphasizes the constant and close interaction of the top and bottom systems. They don’t work in isolation — or in competition — but seamlessly together.”
“the top part of the brain is involved in setting up plans, controlling movements, registering changes in where objects are located in space, and revising plans when anticipated events do not occur.”
“bottom is involved in classifying and interpreting what we perceive, and allows us to confer meaning on the world.”
*http://brainblogger.com/2013/12/19/top-brain-bottom-brain-part-3-the-theory-of-cognitive-modes/ by G. Wayne Miller and Stephen M. Kosslyn, PhD | December 19, 2013
28
Can we take inspiration from the ‘Theory of Cognitive Modes’ to develop a computational model?
Mover Perceiver Simulator Adaptor
http://online.stanford.edu/pgm-fa12
T & B B TT- Top brain, B- Bottom brain
our baby step toward a computational model for perception
(Machine Perception)
29
J. McCarth
y
M. Weiser
D. Engelba
rt J. C. R.
Licklider
Toward a symbiotic partnership between machines and people
htttp://j.mp/k-chehttp://knoesis.org/index.php/Computing_For_Human_Experience
30
RDF OWL
Semantic Sensor Networks (SSN)
How are machines supposed to integrate and interpret sensor data?
31Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
W3C Semantic Sensor Network Ontology
32Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
W3C Semantic Sensor Network Ontology
SSNOntology
2 Interpreted data(deductive)[in OWL] e.g., threshold
1 Annotated Data[in RDF]e.g., label
0 Raw Data[in TEXT]e.g., number
3 Interpreted data (abductive)[in OWL]e.g., diagnosis
Intellego
“150”
Systolic blood pressure of 150 mmHg
ElevatedBlood
Pressure
Hyperthyroidism
less
use
ful …
…
mor
e us
eful
……
33
Levels of Abstraction
34
… and do it efficiently and at scale
What if we could automate this interpretation of Data?
35
Making sense of sensor data with
Henson et al An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web, Applied Ont, 2011
36
People are good at making sense of sensory input
What can we learn from cognitive models of perception?
The key ingredient is prior knowledge
37* based on Neisser’s cognitive model of perception
ObserveProperty
PerceiveFeature
Explanation
Discrimination
1
2
Translating low-level signals into high-level knowledge
Focusing attention on those aspects of the environment that provide useful information
Prior Knowledge
Convert large number of observations to semantic abstractions that provide insights and translate into
decisions
Perception Cycle*
38
To enable machine perception,
Semantic Web technology is used to integrate
sensor data with prior knowledge on the Web
W3C SSN XG 2010-2011, SSN Ontology
39
W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph
Prior knowledge on the Web
40
W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph
Prior knowledge on the Web
41
ObserveProperty
PerceiveFeature
Explanation1
Translating low-level signals into high-level knowledge
Explanation is the act of choosing the objects or events that best account for a set of observations; often referred to as hypothesis building
Explanation
42
Inference to the best explanation• In general, explanation is an abductive
problem; and hard to compute
Finding the sweet spot between abduction and OWL• Single-feature assumption* enables use
of OWL-DL deductive reasoner
* An explanation must be a single feature which accounts for
all observed properties
Explanation is the act of choosing the objects or events that best account for a set of observations; often referred to as hypothesis building
Representation of Parsimonious Covering Theory in OWL-DL
Explanation
43
ExplanatoryFeature ≡ ssn:isPropertyOf∃ —.{p1} … ssn:isPropertyOf⊓ ⊓ ∃ —.{pn}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Observed Property Explanatory Feature
Explanatory Feature: a feature that explains the set of observed properties
Explanation
44
ObserveProperty
PerceiveFeature
Explanation
Discrimination2
Focusing attention on those aspects of the environment that provide useful information
Discrimination is the act of finding those properties that, if observed, would help distinguish between multiple explanatory
features
Discrimination
45
ExpectedProperty ≡ ssn:isPropertyOf.{f∃ 1} … ssn:isPropertyOf.{f⊓ ⊓ ∃ n}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Expected Property Explanatory Feature
Expected Property: would be explained by every explanatory feature
Discrimination
46
NotApplicableProperty ≡ ¬ ssn:isPropertyOf.{f∃ 1} … ¬ ssn:isPropertyOf.{f⊓ ⊓ ∃ n}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Not Applicable Property Explanatory Feature
Not Applicable Property: would not be explained by any explanatory feature
Discrimination
47
DiscriminatingProperty ≡ ¬ExpectedProperty ¬NotApplicableProperty⊓
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Discriminating Property Explanatory Feature
Discriminating Property: is neither expected nor not-applicable
Discrimination
48
Semantic scalability: Resource savings of abstracting sensor data
Orders of magnitude resource savings for generating and storing relevant abstractions vs. raw observations.
Relevant abstractions
Raw observations
Qualities-High BP-Increased Weight
Entities-Hypertension-Hypothyroidism kHealth
Machine Sensors
Personal Input
EMR/PHRComorbidity risk score e.g., Charlson Index
Longitudinal studies of cardiovascular risks
- Find risk factors- Validation - domain knowledge - domain expert
Find contribution of each risk
factor
Risk Assessment Model
Current Observations-Physical-Physiological-History
Risk Score (e.g., 1 => continue3 => contact clinic)
Model CreationValidate correlations
Historical observations e.g., EMR, sensor observations
49
Risk Score: from Data to Abstraction and Actionable Information
50
Use of OWL reasoner is resource intensive (especially on resource-constrained devices), in terms of both memory and time
• Runs out of resources with prior knowledge >> 15 nodes
• Asymptotic complexity: O(n3)
How do we implement machine perception efficiently on a
resource-constrained device?
51
intelligence at the edge
Approach 1: Send all sensor observations to the cloud for processing
Approach 2: downscale semantic processing so that
each device is capable of machine perception
52
0101100011010011110010101100011011011010110001101001111001010110001101011000110100111
Use bit vector encodings and their operations to encode prior knowledge and execute semantic reasoning
Henson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices, ISWC 2012.
Efficient execution of machine perception
53
O(n3) < x < O(n4) O(n)
Efficiency Improvement
• Problem size increased from 10’s to 1000’s of nodes
• Time reduced from minutes to milliseconds• Complexity growth reduced from polynomial
to linear
Evaluation on a mobile device
54
2 Prior knowledge is the key to perceptionUsing SW technologies, machine perception can be formalized and integrated with prior knowledge on the Web
3 Intelligence at the edgeBy downscaling semantic
inference, machine perception can execute efficiently on resource-constrained devices
1 Translate low-level data to high-level knowledge
Machine perception can be used to convert low-level sensory signals into high-level knowledge useful for decision making
Semantic Perception for smarter analytics: 3 ideas to takeaway
kHealthKnowledge-enabled Healthcare
Applied to ADHF, Asthma, GI, and Dementia
55
Brief Introduction Video
57
Through physical monitoring and analysis, our cellphones could act as an early warning system to detect serious health conditions, and provide actionable information
canary in a coal mine
Empowering Individuals (who are not Larry Smarr!) for their own health
kHealth: knowledge-enabled healthcare
60
1http://www.nhlbi.nih.gov/health/health-topics/topics/asthma/2http://www.lung.org/lung-disease/asthma/resources/facts-and-figures/asthma-in-adults.html 3Akinbami et al. (2009). Status of childhood asthma in the United States, 1980–2007. Pediatrics,123(Supplement 3), S131-S145.
25 million
300 million
$50 billion
155,000
593,000
People in the U.S. are diagnosed with asthma (7 million are children)1.People suffering from asthma worldwide2.
Spent on asthma alone in a year2
Hospital admissions in 20063
Emergency department visits in 20063
Asthma: Severity of the problem
61
what can we do to avoid asthma episode?
Real-time health signals from personal level (e.g., Wheezometer, NO in breath, accelerometer, microphone), public health (e.g., CDC, Hospital EMR), and population level (e.g., pollen level, CO2) arriving continuously in fine grained samples potentially with missing information and uneven sampling frequencies.
Variety Volume
VeracityVelocity
ValueWhat risk factors influence asthma control?What is the contribution of each risk factor?
sem
antic
s Understanding relationships betweenhealth signals and asthma attacksfor providing actionable information
WHY Big Data to Smart Data: Asthma example
kHealth: Health Signal Processing Architecture
Personal level Signals
Public level Signals
Population level Signals
Domain Knowledge
Risk Model
Events from Social Streams
Take Medication before going to work
Avoid going out in the evening due to high pollen levels
Contact doctor
AnalysisPersonalized Actionable
Information
Data Acquisition & aggregation
62
63
Asthma Domain Knowledge
Domain Knowledge
ICS= inhaled corticosteroid, LABA = inhaled long-acting beta2-agonist, SABA= inhaled short-acting beta2-agonist ; *consider referral to specialist
Asthma Control and Actionable Information
64
Patient Health Score (diagnostic)
Risk assessment model
Semantic Perception
Personal level Signals
Public level Signals
Domain Knowledge
Population level Signals
GREEN -- Well Controlled YELLOW – Not well controlledRed -- poor controlled
How controlled is my asthma?
65
Population Level
Personal
Wheeze – YesDo you have tightness of chest? –Yes
Observations Physical-Cyber-Social System Health Signal Extraction Health Signal Understanding
<Wheezing=Yes, time, location><ChectTightness=Yes, time, location>
<PollenLevel=Medium, time, location>
<Pollution=Yes, time, location>
<Activity=High, time, location>
Wheezing
ChectTightness
PollenLevel
Pollution
Activity
Wheezing
ChectTightness
PollenLevel
Pollution
Activity
RiskCategory
<PollenLevel, ChectTightness, Pollution,Activity, Wheezing, RiskCategory><2, 1, 1,3, 1, RiskCategory><2, 1, 1,3, 1, RiskCategory><2, 1, 1,3, 1, RiskCategory><2, 1, 1,3, 1, RiskCategory>
.
.
.
Expert Knowledge
Background Knowledge
tweet reporting pollution level and asthma attacks
Acceleration readings fromon-phone sensors
Sensor and personal observations
Signals from personal, personal spaces, and community spaces
Risk Category assigned by doctors
Qualify
Quantify
Enrich
Outdoor pollen and pollution
Public Health
Patient Health Score (diagnostic): Details
Well Controlled - continueNot Well Controlled – contact nursePoor Controlled – contact doctor
66
Patient Vulnerability Score (prognostic)
Risk assessment model
Semantic Perception
Personal level Signals
Public level Signals
Domain Knowledge
Population level Signals
Patient health Score
How vulnerable* is my control level today?
*considering changing environmental conditions and current control level
67
Sensordrone – for monitoring environmental air quality
Wheezometer – for monitoringwheezing sounds
Can I reduce my asthma attacks at night?What are the triggers? What is the wheezing level?
What is the propensity toward asthma?
What is the exposure level over a day?
Commute to Work
Patient Vulnerability Score (prognostic): Details
Luminosity
CO level
CO in gush during day time
Actionable Information
Personal level Signals
Public level Signals
Population level Signals
What is the air quality indoors?
Sensordrone (Carbon monoxide,
temperature, humidity)
Node Sensor (exhaled Nitric
Oxide)
68
SensorsAndroid Device (w/ kHealth App)
Total cost: ~ $500
kHealth Kit for the application for Asthma management
Along with two sensors in the kit, the application uses a variety of population level signals from the web:
Pollen level Air Quality Temperature & Humidity
69
Usability and decision support trial
Dr. Shalini G. Forbis, MD, MPH
Preliminary insights from patient data
S1 S2
Sensor data QA data
Number of Observations
6/2/2
014
6/3/2014
6/4/2
014
6/5/2
014
6/6/2
014
6/7/2
014
6/8/2
014
6/9/2014
6/10/2
014
6/11/2
014
6/12/2
0140
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Did patient take albuterol last night due to cough or wheeze?
6/2/2
014
6/3/2014
6/4/2
014
6/5/2
014
6/6/2
014
6/7/2
014
6/8/2
014
6/9/2014
6/10/2
014
6/11/2
014
6/12/2
014
6/13/2
0140
0.05
0.1
0.15
0.2
0.25
Exhaled Nitric Oxide
Medication (Albuterol) related to decreasing Exhaled Nitric Oxide
6/2/2014
6/3/2014
6/4/2014
6/5/2014
6/6/2014
6/7/2014
6/8/2014
6/9/2014
6/10/2
014
6/11/2
014
6/12/2
0140
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
How much did asthma or asthma symptoms limit patient's activity to-
day?
6/2/2014
6/3/2014
6/4/2014
6/5/2014
6/6/2014
6/7/2014
6/8/2014
6/9/2014
6/10/2
014
6/11/2
014
6/12/2
014
6/13/2014
0
0.05
0.1
0.15
0.2
0.25
Exhaled Nitric Oxide
Activity limitation related to high exhaled Nitric Oxide
6/2/2
014
6/3/2014
6/4/2
014
6/5/2
014
6/6/2
014
6/7/2014
6/8/2
014
6/9/2
014
6/10/2014
6/11/2014
6/12/2
0140
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Has patient had wheeze, chest tightness, or asthma related
cough today?
41792.6000034259
41793.8430565394
41795.8798923843
41796.9000038657
41798.4715425694
41799.6928319907
41802.4572316551
41803.56436067120
0.05
0.1
0.15
0.2
0.25
Nitric Oxide
Low exhaled Nitric Oxide observed with absence of coughing
6/2/2014
6/3/2014
6/4/2014
6/5/2014
6/6/2014
6/7/2014
6/8/2014
6/9/2014
6/10/2014
6/11/2
014
6/12/2014
0
0.5
1
1.5
2
2.5
Pollen
6/2/2
014
6/3/2
014
6/4/2
014
6/5/2
014
6/6/2014
6/7/2
014
6/8/2
014
6/9/2
014
6/10/2
014
6/11/2014
6/12/2
0140
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
How much did asthma or asthma symptoms limit patient's activity
today?
Activity limitation observed with high pollen activity
75
Two research directions for kHealth asthma with more data…
Root cause analysis Action Recommendation
Find Triggers of AsthmaDerive the cause of asthma attacks for a given patient using statistical techniques + knowledge of asthma and its triggers
Minimize Asthma AttacksModel actions based on the utility theory (cost of actions & its rewards) + knowledge of action consequences
76
• Healthcare: ADFH, Asthma, GI– Using kHealth system
• Traffic Analytics:– Understanding traffic flow
• Social Media Analysis :– Crisis coordination using Twitris
I will use applications in 3 domains to demonstrate
78
Understanding traffic flow variations
79
Vehicular traffic data from San Francisco Bay Area aggregated from on-road sensors (numerical) and incident reports (textual)
http://511.org/
Every minute update of speed, volume, travel time, and occupancy resulting in 178 million link status observations, 738 active events, and 146 scheduled events with many unevenly sampled observations collected over 3 months.
Variety Volume
VeracityVelocity
ValueCan we detect the onset of traffic congestion?Can we characterize traffic congestion based on events?Can we provide actionable information to decision makers?
sem
antic
s Representing prior knowledge of traffic lead to a focused exploration of this massive dataset
Big Data to Smart Data: Traffic Management example
Semantic Annotation using Background Knowledge
Image Credit: http://traffic.511.org/index
slow-moving-traffic
Domain knowledge in the form of traffic vocabulary
Domain knowledge of traffic flow synthesized from sensor data
80
Explained-by
Horizontal operator: relating/mapping data from different modality to a concept (theme) within a spatio-temporal context;Spatial context even include what it means to have a slow traffic for the type of road
81
• Healthcare: ADFH, Asthma, GI– Using kHealth system
• Traffic Analytics:– Understanding traffic flow
• Social Media Analysis :– Crisis coordination using Twitris
I will use applications in 2 domains to demonstrate
82
Image: http://www.gizmodo.com.au/2012/04/how-we-identify-single-voices-in-a-crowd/
BIG QUESTION: Can these needles be identified in the haystack of massive datasets?
Me and @CeceVancePR are coordinating a clothing/food drive for families affected by Hurricane Sandy. If you would like to donate, DM us
Does anyone know how to donate clothes to hurricane #Sandy victims?
[REQUEST/DEMAND]
[OFFER/SUPPLY]Coordination teams
want to hear!
[BIG] Ad-hoc Community with Varying but [FEW] Important Intents
• May lead to second disaster to be managed:– Under-supply of required demands – Over-supply of not required resources
• Hurricane Sandy example, “Thanks, but no thanks”, NPR, Jan 12 2013
Story link:http://www.npr.org/2013/01/09/168946170/thanks-but-no-thanks-when-post-disaster-donations-overwhelm
Uncoordinated Engagement
84
How to volunteer, donate to Hurricane Sandy: <URL>
If you have clothes to donate to those who are victims of Hurricane Sandy …
Red Cross is urging blood donations to support those affected <URL>
I have TONS of cute shoes & purses I want to donate to hurricane victims …
Does anyone know how to donate clothes to hurricane #Sandy victims?
Does anyone know of community service organizations to volunteer to help out?
Needs to get something, suggests scarcity:
REQUEST (demand)Offers or wants to give, suggests abundance:
OFFER (supply)
Matching requests with offers
85
Want to help animals in #Oklahoma? @ASPCA tells how you can help:
http://t.co/mt8l9PwzmOx
RESPONSE TEAMS (including humanitarian
org. and ‘pseudo’ responders)
VICTIM SITE
Where do I go to help out for volunteer work
around Moore?
Anyone know?
Anyone know where to donate to
help the animals from the Oklahoma
disaster? #oklahoma #dogs
Matchable
Matchable
If you would like to volunteer today, help is desperately needed in
Shawnee. Call 273-5331 for
more info
CITIZEN SENSORS
DEMAND SUPPLY
Match-making: Assisting Coordination
Image: http://offthewallsocial.com/tag/social-media/
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Two excellent videos• Vinod Khosla:
the Power of Storytelling and the Future of Healthcare
• Larry Smarr: The Human Microbiome and the Revolution in Digital Health
Wrapping up: For more on importance of what we talked about
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• Big Data is every where– at individual and community levels - not just
limited to corporation – with growing complexity: Physical-Cyber-Social
• Analysis is not sufficient• Need interaction between bottom up
techniques and top down processing
Wrapping up: Take Away
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Wrapping up: Take Away
• Focus on Humans and Improve human life and experience with SMART Data.– Data to Information to Personally and Contextually
Relevant Abstractions (Semantic Perception)– Actionable Information (Value from data) to assist and
support human in decision making.
• Focus on Value -- SMART Data– Big Data Challenges without the intention of deriving
Value is a “Journey without GOAL”.
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Amit Sheth’s PHD students
Ashutosh Jadhav*
Hemant Purohit
Vinh Nguyen
Lu ChenPavan Kapanipathi
*
Pramod Anantharam
*
Sujan Perera
Maryam Panahiazar
Sarasi Lalithsena
Shreyansh Batt
Kalpa Gunaratna
Delroy Cameron
Sanjaya Wijeratne
Wenbo Wang
Special thanks: Pramod. This presentation covers some of the work of my PhD students. Key contributors: Pramod Anantharam, Cory Henson and TK Prasad.
Special thanks
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• Among top universities in the world in World Wide Web (cf: 10-yr impact, Microsoft Academic Search: among top 10 in June2014)
• Among the largest academic groups in the US in Semantic Web + Social/Sensor Webs, Mobile/Cloud/Cognitive Computing, Big Data, IoT, Health/Clinical & Biomedicine Applications
• Exceptional student success: internships and jobs at top salary (IBM Watson/Research, MSR, Amazon, CISCO, Oracle, Yahoo!, Samsung, research universities, NLM, startups )
• 100 researchers including 15 World Class faculty (>3K citations/faculty avg) and ~45 PhD students- practically all funded
• Extensive research for largely multidisciplinary projects; world class resources; industry sponsorships/collaborations (Google, IBM, …)
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Top organization in WWW: 10-yr Field Rating (MAS)
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Smart Data - How you and I can and will exploit Big Data http://knoesis.org
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thank you, and please visit us athttp://knoesis.org
Smart Data - How you and I will exploit Big Data