eventshop 120721

8/17/2012 1

Ramesh Jain

with

Several Collaborators

Scarcity: inadequate supply, Insufficiency of amount or supply

Abundance: an extremely plentiful or oversufficient quantity or supply

8/17/2012 Proprietary and Confidential, Not For

Distribution 2


Distribution 3

Scarcity


Distribution 4

Abundance


Distribution 5


Distribution 6


Distribution 7

People

Things

Events

We are immersed in Networks of

It is now possible to be Pansophical. 8/17/2012 8

8/17/2012 9

Past is EXPERIENCE

Present is EXPERIMENT

Future is EXPECTATION

Use your Experiences

In your Experiments

To achieve your Expectations

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Astrology

To

Astronomical Volumes of Data


Distribution 11

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Have been reporting events as micro-blogs

Sensors and Internet of Things are creating and reporting even more events than humans are.

Objects -- popular in the West.

Relationships and Events – popular in the East.

Objects and Events – seems to be the new trend.

The Web has re-emphasized the importance of every object and event being connected to others -- East Meets West.

Data

Objects

Relationships and Events

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Knowledge Observe

Recognize

Act

Big Data

Planning Control

Objects Situations

Take place in the real world.

Captured using different sensory mechanism.

Each sensor captures only a limited aspect of the event.

Can be used to bridge the semantic gap.

Conferences Days

Sessions Talks Purpose of the talk

Wedding An Earthquake The Big Bang 9/11 Formation of Google Media Lab Trip Me

My Birth, Being here, and Dying in 100 years.

People Things Places Time Experiences Events

E by Westerman and Jain

E* by Gupta and Jain

Connecting

People

Massive collection of events.

Facebook reports 20 Billion updates – 3 Billion Photos –

each month.

Reporting events as micro-blogs

Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?

Time

Atomic and Composite Events

Current Social Networks

Important Unsatisfied Needs

8/17/2012 26

Middle 3.5 Billion

The World as seen through Mobile Phones

Top 1.5 Billion

Bottom 2 Billion

Middle of the Pyramid (MOP):

Ready, BUT …

Most attention by Technologists – so far.

Not Ready

Resources

Physical: food, water, goods, …

Informational: Wikipedia, Doctors, …

Transportation

Employment

Spiritual

Timeliness

Efficiency

Connecting

People

And

Resources

Aggregation

and

Composition

Situation

Detection Alerts

Queries

Information

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Distribution 30

Atomic Composite

Static

Dynamic

Object

Event

Scene

Situation

Situation: An actionable abstraction of observed spatio-temporal characteristics

Allow users to define their own spatio-temporal features and create the situation detection filters.

8/17/2012 32

Level 1: Unified representation

(STT Data)

Level 3: Symbolic rep. (Situations)

Properties

Properties

Properties

Level 0: Raw data streams e.g. tweets, cameras, traffic, weather, …

Level 2: Aggregation

(Emage)

…

STT Stream

Emage

Situation

(a) Pollen levels (Source: Visual) (b) Census data (Source: text file) (c) Reports on ‘Hurricanes’ (source: Twitter stream)

d) Cloud cover (Source: Satellite imagery) (e) Predicted hurricane path (source: KML) (f) Open shelters coverage(Source: KML)

Representation for different data sources into a common spatio-temporal format.

S. No Operator Input Output

1 Selection Temporal E-mage Set

Temporal E-mage Set

2 Arithmetic & Logical

K*Temporal E-mage Set

Temporal E-mage Set

3 Aggregation α Temporal E-mage set Temporal E-mage Set

4 Grouping Temporal E-mage Set Temporal E-mage Set

5 Characterization :

•Spatial

•Temporal

•Temporal E-mage Set

•Temporal Pixel Set



6 Pattern Matching

•Spatial

•Temporal

•Temporal E-mage Set



•Temporal Pixel Set 35

8/17/2012 35

Front End GUI

NewData

Source

NewQuery

E-mageStream

E-mage Stream

E-mage Stream

Data Cloud

Back End Controller

Stream Query Processor

Data IngestorRegistered

DataSources

RegisteredQueries

Raw Spatial Data Stream

API Calls

Raw DataStorage

Personalized Alert Unit

AlertRequest

User Info

8/17/2012 36

Experimentation is essential to deal with evolving unstructured sensory data. Inspired by Photoshop.

8/17/2012 37

Business decision making: Demand-supply analysis, opening a new store, offer,…

Medical : Epidemic monitoring, Asthma, pollution effect mitigation

Disaster relief: (hurricane, flood, fire) directing people to appropriate resources.

Traffic: Suggesting best routes

Election

8/17/2012 38 Proprietary and Confidential, Not For

Distribution


Distribution


Distribution

Retail Store Locations

Net Catchment area


Distribution 41

e.g. High Flu risk

+

1) Macro situation

Social sensors

Device sensors

Macro sensors

Personal life streams

Profile/ Preferences

+

2) Personalized

situation

+

Planetary scale sensing

Personal context

Available resources

3) Recommend

Actions

Resource data

into ‘high’ and ‘low ’activity zones.


Distribution

Situational controller

•Goal •Macro Situation •Rules

Micro event e.g. “Arrgggh, I

have a sore throat”

(Loc=New York, Date=12/09/10)

Macro situation

Control Action “Please visit nearest CDC

center at 4th St immediately”

Date=12/09/10

Alert Level=High

Level 1 personal threat + Level 3 Macro threat -> Immediate action 8/17/2012 44

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Flood level - Shelter

Flood Level Shelter

Twitter

Classify (Flood level - Shelter)

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Distribution 48

1. Alert me when major Allergy outbreak happens in my location !

2. How healthy is today for me ?

3. What is the best location for me to undertake outdoor activities?

Allergy Threat Level

ϵ {low, mid, high}

Air quality

Emage (air quality index)

Δ

Weather.com

Pollen count Tweet reports

US, 24 hrs,

1X1lat long

Emage (pollen level)

Δ

Pollen.com

Emage (number of reports)

Air quality Pollen count

US, 24 hrs,

1X1 lat long

US, 24 hrs,

1 X 1 lat long

S-t-t (#reports)

Δ

Δ

Twitter

Twitter.com

US, 24 hours,

2X2

Personal asthma threat ϵ {low,

mid, high}

Heart rate

∏

Cardio device

Sneezing severity Asthma threat level

Sensor stream Twitter

∏

∏

Pollen.com, AQI.com, Twitter

EventShop

Twitter.com

Thresholds Low:{0, 0.3],

Mid: {0.3, 0.7], High: {0.7,1}


Distribution 53

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Framework tested using applications: Store location

Political campaign

Flu monitoring

EventShop system: Operators implemented:

Selection , Arithmetic & Logical, Aggregation , Grouping, Characterization (spatial + temporal), Pattern Matching (spatial + temporal)

Applications tested: Thai flood relief

Hurricane alerts

Safe locations for Asthmatic patients

Scalability

Data discovery

Application discovery

Conceptual modeling of situations

Richer operation set

User experience


Distribution 55

Make EventShop Robust

Develop system to deal with BIG DATA

Experiment with many applications


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eventshop 120721

Technology