Jeff jonas big data new physics

Big Data. New Physics.And Geospatial “Superfood”

© 2014 IBM Corporation1111

Jeff Jonas, Jeff Jonas, Jeff Jonas, Jeff Jonas, IBM FellowChief Scientist, Context Computing

Email: [email protected]: www.jeffjonas.typepad.com

Twitter: http://www.twitter.com/jeffjonas

About the Speaker

� Jeff Jonas� IBM Fellow, Chief Scientist for Context Computing

� Founder and Chief Scientist of Systems Research & Development (SRD), acquired by IBM in 2005


acquired by IBM in 2005� Been designing, building deploying entity resolution systems for three decades

� This technology is used today by defense & intelligence, financial institutions, humanitarian efforts and more

� Today: Primarily focused on ‘sensemaking on streams’ with special attention towards privacy and civil liberties protections

”The data must find the data and the

relevance must find the user.”


relevance must find the user.”

Com

puting Pow

er Growth

Available Observation

Space

Context

Trend: Organizations Are Getting Dumber

EnterpriseAmnesia


Time

Com

puting Pow

er Growth

Sensemaking Algorithms


Space

ContextWHY?

Trend: Organizations Are Getting Dumber

Com

puting Pow

er Growth


Time

Sensemaking AlgorithmsC

ompu

ting Pow

er Growth

Algorithms at Dead End.

You Can’t


You Can’t Squeeze Knowledge

Out of a Pixel.

No Context

[email protected]


Context, definition

Better understanding something


Better understanding something by taking into account the things around it.

I ducked as the bat flew my way.

Another exciting baseball game …


Information in Context … and Accumulating

Top 200CustomerTwitter

[email protected]

LinkedInCareer History


Customer

JobApplicant

TwitterInfluencer

AMLInvestigation

The Puzzle Metaphor

� Imagine an ever-growing pile of puzzle pieces of varying sizes, shapes and colors

� What it represents is unknown – there is no picture on hand

� Is it one puzzle, 15 puzzles, or 1,500 different puzzles?


� Some pieces are duplicates, missing, incomplete, low quality, or have been misinterpreted

� Some pieces may even be professionally fabricated lies

� Until you take the pieces to the table and attempt assembly, you don’t know what you are dealing with

270 pieces90%

200 pieces66%

150 pieces50%

6 pieces2%

Puzzling Images: Courtesy Ravensburger © 2011


90% 66% 50% 2%

30 pieces10% (duplicates)

First Discovery


More Data Finds Data


Duplicates in Front Of Your Eyes


First Duplicate Found Here


Incremental Context – Incremental Discovery

6:40pm START

22min “Hey, this one is a duplicate!”

35min “I think some pieces are missing.”


37min “Looks like a bunch of hillbillies ona porch.”

44min “Hillbillies, playing guitars, sittingon a porch, near a barber sign …and a banjo!”

150 pieces

50%


Incremental Context – Incremental Discovery

47min “We should take the sky and grassoff the table.”

2hr “Let’s switch sides, and see if wecan make sense of this fromdifferent perspectives.”


different perspectives.”

2hr10m “Wait, there are three … no, fourpuzzles.”

2hr17m “We need a bigger table.”

2hr18m “I think you threw in a few randompieces.”

How Context Accumulates

� With each new observation … one of three assertions are made: 1) Un-associated; 2) placed near like neighbors; or 3) connected

� Must favor the false negative

� New observations sometimes reverse earlier assertions


� Some observations produce novel discovery

� The emerging picture helps focus collection interests

� As the working space expands, computational effort increases

� Given sufficient observations, there can come a tipping point

� Thereafter, confidence improves while computational effort decreases!

Unique Iden

tities

Overstated Population


Observations

Unique Iden

tities

True Population

Counting Is Difficult

Mark Smith6/12/1978

Mark R Smith(707) 433-0000DL: 00001234


6/12/1978443-43-0000

File 1

File 2

Unique Iden

tities

The Rise and Fall of a Population


Observations

Unique Iden

tities

True Population

Data Triangulation

New Record

Mark Smith6/12/1978

Mark R Smith(707) 433-0000DL: 00001234


Mark Randy Smith443-43-0000DL: 00001234

6/12/1978443-43-0000

File 1

File 2

Big Data [in context]. New Physics.

�More data: better the predictions– Lower false positives

– Lower false negatives


�More data: bad data good– Suddenly glad your data is not perfect

�More data: less compute

Big Data


Pile of ____ Information In Context

One Form of Context: “Expert Counting”

� Is it 5 people each with 1 account … or is it 1 person with 5 accounts?

� Is it 20 cases of H1N1 in 20 cities … or one case reported 20 times?


reported 20 times?

� If one cannot count … one cannot estimate vector or velocity (direction and speed).

�Without vector and velocity … prediction is nearly impossible.

Entity ResolutionDemonstration


Entity Resolution Demonstration

DECEASED PERSONDECEASED PERSONDECEASED PERSONDECEASED PERSONGeorge BalstonYOB: 1951 SSN: 5598DOD: 1995

VOTERVOTERVOTERVOTERGeorge F BalstonYOB: 1951 D/L: 480113070 SW Karen Blvd Apt 7 Beaverton, OR 97005Last voted: 2008


When it comes to best practices in voter matching, if only a name and year of birth match, this is insufficient proof of a match. Many different people in the

U.S. share a name and year of birth.

Human review is required.

Unfortunately, there can be many thousands of cases just like this and state election offices don’t have the staff/budget to manually review them all.

Now Consider This Tertiary DMV Record




DMVDMVDMVDMVGeorge F BalstonYOB: 1951 SSN: 5598 D/L: 48013043 SW Clementine Blvd Apt 210Beaverton, OR 97005

The DMV record contains enough features to match both the voter (name, year of birth and driver’s license) and/or the deceased persons record (name, year of birth and SSN). For the sake of argument, let’s say it matches the voter best.


Features Accumulate


DMVDMVDMVDMV


The voter/DMV record now shares a name, year of birth and SSN with the deceased person. In voter matching best practices, this evidence would be

sufficient to make a determination that this voter is likely deceased. This case no longer needs human review.


VOTERVOTERVOTERVOTERGeorge F BalstonYOB: 1951 D/L: 480113070 SW Karen Blvd Apt 7Beaverton, OR 97005Last voted: 2008

DMVDMVDMVDMV

As features accumulate it becomes possible to resolve previous un-resolvable

identity records.

As events and transactions

Useful Insight Revealed!Useful Insight Revealed!




As events and transactions accumulate – detection of

relevance improves.

Here we can see George who died in 1995 voted in 2008.

Expert Counting: Degrees of Difficulty

IncompatibleFeatures

Deceit

Bob Jones123455

Ken Wells550119


Exactly Same

Fuzzy

Bob Jones123455

Bob Jones123455

Bob Jones123455

Robert T Jonnes000123455

Bob Jones123455

bjones@hotmail

Deceit Detection Using Context Accumulation

Deceit

Bob Jones123455

Ken Wells550119Robert Jones

123455POB 13452DOB 03/12/73

Feature Accumulation


Ken Wells550119POB 999911DOB 03/12/[email protected]

[email protected] 03/12/73Robert Jones123455Ken Wells550119

Resolved!

DOB 03/12/73

Bob JonesPOB [email protected]

Skilled adversaries use “channel separation” to avoid detection.


Cell Phone #1

Unknown

Cell Phone #2

Unknown

Passport #1

William A.

Bank Acct #1

Billy K.

Detection requires “channel consolidation.”


William Aaka Billy K.• Cell Phone #1• Cell Phone #2• Bank Acct #1• Passport #1

Take Note

To catch clever criminals, one must ...

1) Collect observations the adversary doesn’t


1) Collect observations the adversary doesn’t know you have

2) Or, be able to perform compute over your observations in a manner the adversary cannot fathom

InfoSphere Identity Insightv8


v8

New Think About Expert Counting

IncompatibleFeatures

Deceit

Bob Jones123455

Ken Wells550119


Exactly Same

Fuzzy

Bob Jones123455

Bob Jones123455

Bob Jones123455

Robert T Jonnes000123455

Bob Jones123455

bjones@hotmail

Key Features Enable Expert Counting

Name License Plate No. Serial NumberAddress VIN MAC AddressDate of Birth Make IP AddressPhone Model MakePassport Year Model

People Cars Router


Passport Year ModelNationality Color Firmware VersionBiometric Etc. Etc.Etc.

Consider Lying Identical Twins

#123Sue3/3/84UberstanExp 2011

PASSPORT#123Sue3/3/84UberstanExp 2011

PASSPORT


Fingerprint

DNAMost TrustedAuthority

“Same person –trust me.”

Most TrustedAuthority

�The same thing cannot be in two places … at the same time.

�Two different things cannot occupy the same space … at the


�Two different things cannot occupy the same space … at the same time.

Space & Time Enables Absolute Disambiguation

When When WhenWhere Where Where

People Cars RouterName License Plate No. Serial NumberAddress VIN MAC AddressDate of Birth Make IP AddressPhone Model MakePassport Year Model


Passport Year ModelNationality Color Firmware VersionBiometric Etc. Etc.Etc.

“Life Arcs” Are Also Telling

Bill Smith4/13/67

Salem, Oregon

Bill Smith4/13/67

Seattle, Washington

Address History Address History


Address History

Tampa, FL 2008-2008

Biloxi, MS 2005-2008

NY, NY 1996-2005

Tampa, FL 1984-1996

Address History

San Diego, CA 2005-2009

San Fran, CA 2005-2005

Phoenix, AZ 1990-2005

San Jose, CA 1982-1990

OMG


Space-Time-Travel

� Cell phones are generating a staggering amount of geo-locational data – 600B transactions per day being created in the US alone

� This data is being “de-identified” and shared with third parties – in volume and in real-time


parties – in volume and in real-time

� Your movement quickly reveals where you spend your time (e.g., evenings vs. working hours)

� Re-identification (figuring out who is who) is somewhat trivial

� And, oh so powerful predictions …

The 10 People I Spend the Most Time With(Not at Home and Not at Work)

1. Michelle J2. Renee M3. Peggy M4. Erin E5. Joshua J

He must be following me!


4. Erin E5. Joshua J6. Ivan X7. Bob Y8. Amanda H9. Dane J10. Wesley R

He must be following me!

Consequences

� Space-time-travel data is the ultimate biometric

� It will enable enormous opportunity

� It will unravel one’s secrets


� It will unravel one’s secrets

� It will challenge existing notions of privacy

� Adoption is now accelerating at a blistering pace

[Theatrical Pause]


[Theatrical Pause]

The G2 | Sensemaking Project


The G2 Vision

1) Evaluate each new observation against previous observations.

2) Determine if what is being observed is relevant.

3) Delivering this actionable insight to its consumer


3) Delivering this actionable insight to its consumer … fast enough to do something about it while it is still happening.

4) Doing this with sufficient accuracy and scale to really matter.

Uniquely G2

� Real “Context Computing”– Complete Context: Contextualize diverse observations, each observation benefiting from others

– Current Context: Real-time, incremental integration

– Conflicting Context: High tolerance for disagreement, confusion and uncertainty

– Self-Correcting Context: New observations able to reverse earlier assertions

� Engineered ground-up for cloud compute … in support of hemisphere-scale data


� Introduce new data sources (e.g., geospatial), new entity types (e.g., vessels), new features (e.g.,MAC addresses) … without schema change/re-engineering

� From sense to respond in sub-200ms– fast enough to do something about the transaction while it is still happening

� Unprecedented number of Privacy by Design (PbD) features baked-in

Privacy by Design (PbD)

1. Full Attribution

2. Tamper Resistant Audit Log

3. Information Transfer Accounting

4. Data Tethering


http://jeffjonas.typepad.com/jeff_jonas/2012/06/privacy-by-design-in-the-era-of-big-data.html

4. Data Tethering

5. False Negative Favoring

6. Self-Correcting False Positives

7. Analytics on Anonymized Data

Example: Self-Correcting False Positive

John T Smith Jr123 Main Street703 111-2000

DOB: 03/12/1984

John T Smith123 Main Street

A plausible claim these two people are the same

1

2 John T Smith Sr123 Main Street

Until this record

3


Which reveals this is a FALSE POSITIVE

123 Main Street703 111-2000DL: 009900991

2123 Main Street703 111-2000DL: 009900991

Until this record comes into view

Example: Self-Correcting False Positive

John T Smith Jr123 Main Street703 111-2000

DOB: 03/12/1984

John T Smith123 Main Street

John T Smith Sr123 Main Street

1

3

2


123 Main Street703 111-2000DL: 009900991

123 Main Street703 111-2000DL: 009900991

New Best Practice:FIXED IN REAL-TIME

(not end of month)

John T Smith123 Main Street703 111-2000DL: 009900991

2

2

Use Cases

� Maritime Domain AwarenessNew system lets authorities track suspicious ships

http://www.asiaone.com/print/News/Latest%2BNews/Science%2Band%2BTech/Story/A1Story20130703-434337.html

� Voter Registration Modernization


� Voter Registration ModernizationDavid Becker (PEW Charitable Trust) and Jeff Jonas (IBM) Discuss How G2 Has Helped

Modernize Voter Registration in America

http://ibmreferencehub.com/STG/ibm_executive_edge_2013/#gensession_daytwo_jonasbecker

Closing Thoughts



Space

Context

Wish This on the Adversary

EnterpriseAmnesia

Com

puting Pow

er Growth


Time


ompu

ting Pow

er Growth

Wish This for Yourself: Better Sensemaking Skills


Space

Context

Com

puting Pow

er Growth


Time


ompu

ting Pow

er Growth

State of the Union: Isolated Analytics

Structured Data Analytics

Unstructured Data Analytics


ObservationSpace

Action

Social Network Analytics

The Future: General Purpose Context Accumulation

Data Finds Data Relevance Finds You

This is GThis is GThis is GThis is G2222


ObservationSpace

Consumer(An analyst, a system, the sensor itself, etc.)

InformationIn Context

The most competitive organizations

are going to make sense of what they are observing

fast enough to do something about it


fast enough to do something about it

while they are observing it.

Related Blog Posts

Algorithms At Dead-End: Cannot Squeeze Knowledge Out Of A Pixel

Puzzling: How Observations Are Accumulated Into Context

Big Data. New Physics.

On A Smarter Planet … Some Organizations Will Be Smarter-er Than Others


Your Movements Speak for Themselves: Space-Time Travel Data is Analytic Super-Food!

When Federated Search Bites

Data Finds Data

Structuring Unstructured Data

Fantasy Analytics

Questions?


Email: [email protected]

Blog: www.jeffjonas.typepad.com


Big Data. New Physics.And Geospatial “Superfood”


Jeff Jonas, Jeff Jonas, Jeff Jonas, Jeff Jonas, IBM FellowChief Scientist, Context Computing

Email: [email protected]: www.jeffjonas.typepad.com


Jeff jonas big data new physics

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