Smart Data - How you and I will exploit Big Data for personalized digital health and many other activities

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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

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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

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Variety – not just structure but modality: multimodal, multisensory

Structured

Unstructured

Semi structured

Audio

Video

Images

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Velocity

Fast Data

Rapid Changes

Real-Time/Stream Analysis

Current application examples: financial services, stock brokerage, weather tracking, movies/entertainment and online retail

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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

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“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

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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

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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

Wheeze

Luminosity

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

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• 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

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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

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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)

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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

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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”

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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.

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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

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‘BY human’: Involving Crowd Intelligence in data processing

Crowdsourcing and Domain-expert guided Machine Learning Modeling

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Detection of events, such as wheezing sound, indoor temperature, humidity,

dust, and CO level

Weather Application

Asthma 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 Application

Power Monitoring Application

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‘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.

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Big Data is pervasive - It is Smart Data that matter!

DATAObservations from machine and social sensors

KNOWLEDGEfor interpretation of

observations

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ACTIONSsituation awareness useful

for decision making

Primary challenge is to bridge the gap between data and actions

Contextualization

Personalization

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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

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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)

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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

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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

……

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Levels of Abstraction

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… 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

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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*

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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

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W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph

Prior knowledge on the Web

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W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph

Prior knowledge on the Web

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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

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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

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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

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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

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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

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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

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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

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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/PHR

Comorbidity 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

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Risk Score: from Data to Abstraction and Actionable Information

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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?

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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

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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

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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

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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

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Brief Introduction Video

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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

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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

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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

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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

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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?

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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

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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

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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)

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Sensors

Android 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

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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

6/3/2

014

6/4/2

014

6/5/2

014

6/6/2

014

6/7/2

014

6/8/2

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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

6/3/2

014

6/4/2

014

6/5/2

014

6/6/2

014

6/7/2

014

6/8/2

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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

6/2/2

014

6/3/2

014

6/4/2

014

6/5/2

014

6/6/2

014

6/7/2

014

6/8/2

014

6/9/2

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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?

6/2/2

014

6/3/2

014

6/4/2

014

6/5/2

014

6/6/2

014

6/7/2

014

6/8/2

014

6/9/2

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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

Activity limitation related to high exhaled Nitric Oxide

6/2/2

014

6/3/2

014

6/4/2

014

6/5/2

014

6/6/2

014

6/7/2

014

6/8/2

014

6/9/2

014

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

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

6/2/2

014

6/3/2

014

6/4/2

014

6/5/2

014

6/6/2

014

6/7/2

014

6/8/2

014

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014

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014

6/12/2

0140

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

6/6/2

014

6/7/2

014

6/8/2

014

6/9/2

014

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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

today?

Activity limitation observed with high pollen activity

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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

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• 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

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Understanding traffic flow variations

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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

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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

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• 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

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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

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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

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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 at

http://knoesis.org

Smart Data - How you and I will exploit Big Data