The Information Avalanche: Reducing Information Overload

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The Information Avalanche:Reducing Information Overload

Jim GrayMicrosoft Research

Onassis Foundation Science Lecture Serieshttp://www.forth.gr/onassis/lectures/2002-07-15/index.html

Heraklion, Crete, Greece, 15-19 July 2002

http://www.forth.gr/onassis/lectures/2002-07-15/index.html

2

Thesis

• Most new information is digital(and old information is being digitized)

• A Computer Science Grand Challenge:– Capture– Organize– Summarize– Visualize

This information• Optimize Human Attention as a resource.• Improve information quality

3

Information Avalanche• The Situation – a census of the data

– We can record everything– Everything is a LOT!

• The Good news– Changes science, education, medicine, entertainment,….– Shrinks time and space– Can augment human intelligence

• The Bad News– The end of privacy– Cyber Crime / Cyber Terrorism– Monoculture

• The Technical Challenges– Amplify human intellect– Organize, summarize and prioritize information– Make programming easy.

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How much information is there?

• Soon everything can be recorded and indexed

• Most bytes will never be seen by humans.

• Data summarization, trend detection anomaly detection are key technologies

See Mike Lesk: How much information is there: http://www.lesk.com/mlesk/ksg97/ksg.html

See Lyman & Varian:

How much informationhttp://www.sims.berkeley.edu/research/projects/how-much-info/

Yotta

Zetta

Exa

Peta

Tera

Giga

Mega

KiloA BookA Book

.Movie

All LoC books(words)

All Books MultiMedia

Everything!

Recorded

A PhotoA Photo

24 Yecto, 21 zepto, 18 atto, 15 femto, 12 pico, 9 nano, 6 micro, 3 milli

http://www.lesk.com/mlesk/ksg97/ksg.html

http://www.sims.berkeley.edu/research/projects/how-much-info/



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PB

EB

TB

Media TB/y Growth Rate, %

optical 50 70

paper 100 2

film 100,000 4

magnetic 1,000,000 55

total 1,100,150 50

• ~10 Exabytes

• ~90% digital

• > 55% personal

• Print: .003% of bytes5TB/y, but text has lowest entropy

• Email is (10 Bmpd) 4PB/y and is 20% text (estimate by Gray)

• WWW is ~50TBdeep web ~50 PB

• Growth: 50%/y

Information CensusLesk Varian & Leyman

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TB/y by media log scale (median estimate from Lyman/Varian)

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

Terabytes/Year Median Estimate

TB

/y

magnetic

film

paper

optical

Growth Rate, %

0

10

20

30

40

50

60

70

80

optical paper film magnetic total

TB/y by media (median estimate from Lyman/Varian)

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

Terabytes/Year Median Estimate

TB

/y

magnetic

film

paper

optical

93%

7

1E+3

1E+4

1E+5

1E+6

1E+7

1988 1991 1994 1997 2000

disk TB growth: 112%/y

Moore's Law: 58.7%/y

ExaByte

Disk TB Shipped per Year1998 Disk Trend (J im Porter)

http://www.disktrend.com/pdf/portrpkg.pdf.Storage capacity beating Moore’s law

• Improvements:Capacity 60%/yBandwidth 40%/yAccess time 16%/y

• 1000 €/TB today• 100 €/TB in 2007

Moores law 58.70% /year

Revenue 7.47%TB growth 112.30% since 1993

Price decline 50.70% since 1993

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Disk Storage Cheaper than Paper• File Cabinet: cabinet (4 drawer) 250$

paper (24,000 sheets) 250$space (2x3 @ 10€/ft2) 180$

total 700$0.03 €/sheet

• Disk: disk (160 GB =) 200$ASCII: 500 m pages 2e-7 €/sheet (10,000x cheaper)

Image: 1 m photos 3e-4 €/photo (100x cheaper)

• Store everything on disk

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Why Put Everything in Cyberspace?

Low rentmin $/byte

Shrinks timenow or later

Shrinks spacehere or there

Automate processingknowbots

Point-to-Point OR Broadcast

Imm

edia

te O

R T

ime

Del

ayed

LocateProcessAnalyzeSummarize

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Storage trends• Right now, it’s affordable to buy 100 GB/year• In 5 years you can afford to buy 1TB/year!

(assuming storage doubles every 18 months)

0

0.2

0.4

0.6

0.8

1

2002 2003 2004 2005 2006 2007

Dis

k ca

pac

ity

(TB

)

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Trying to fill a terabyte in a year

Item Items/TB Items/day

300 KB JPEG 3.6M 9800

1 MB Doc 1.0M 2900

1 hour 256 kb/s MP3 audio

9.3K 26

1 hour 1.5 Mbp/s MPEG video

290 4

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MemexAs We May Think, Vannevar Bush, 1945

“A memex is a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility”

“yet if the user inserted 5000 pages of material a day it would take him hundreds of years to fill the repository, so that he can be profligate and enter material freely”

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Gordon Bell’s MainBrain™Digitize EverythingA BIG shoebox?

• Scans 20 k “pages” tiff@ 300 dpi 1 GB• Music: 2 k “tacks” 7 GB• Photos: 13 k images 2 GB • Video: 10 hrs 3 GB• Docs:3 k (ppt, word,..) 2 GB• Mail: 100 k messages 3 GB

18 GB

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Gary Starkweather

• Scan EVERYTHING

• 400 dpi TIFF

• 70k “pages” ~ 14GB• OCR all scans (98% recognition ocr accuracy)

• All indexed (5 second access to anything)

• All on his laptop.

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Access!

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50% personal, What about the other 50%

• Business– Wall Mart online: 1PB and growing….– Paradox: most “transaction” systems have mere

PBs.– Have to go to image/data monitoring for big data

• Government– Online government is big thrust (cheaper, better,…)

• Science

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CERN Tier 0

Instruments: CERN – LHCPeta Bytes per Year

Looking for the Higgs Particle

• Sensors: 1000 GB/s (1TB/s)

• Events 75 GB/s

• Filtered 5 GB/s

• Reduced 0.1 GB/s~ 2 PB/y

• Data pyramid: 100GB : 1TB : 100TB : 1PB : 10PB

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LHC Requirements (2005- )• 1E9 events pa

@ 1MB/ev = 1PB/year/expt• Reconstructed =

100TB/recon/year/expt• Send to Tier1 Regional Centres• => 400TB/year to RAL?• Keep one set + derivatives on disk• …and rest on tape• But UK plans a

Tier1 clone• Many data clones

0

50

100

150

200

250

300

350

2001 2002 2003

Purchased (TB)

Cumulative(TB)

Source: John GordonIT Department, CLRC/RALCUF Meeting, October 2000

2004 2005 2006 2007Processors (PC99 equivalents) 1,560 7,700 11,700 15,600Disks (TB) 16 136 216 296Tapes (PB) 0.08 0.4 1.2 2.0

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Science Data VolumeESO/STECF Science Archive

• 100 TB archive

• Similar at Hubble, Keck, SDSS,…

• ~1PB aggregate

Archive Volume (GB)

1

10

100

1000

10000

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Archive Volume (GB)

1

10

100

1000

10000

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001Data Requested (GB)

1

10

100

1000

10000

1996 1997 1998 1999 2000 2001

Data Requested (GB)

1

10

100

1000

10000

1996 1997 1998 1999 2000 2001

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Data Pipeline: NASA

• Level 0: raw data data stream• Level 1: calibrated data measured values• Level 1A: calibrated & normalized flux/magnitude/…• Level 2: derived data metrics vegetation index

• Data volume – 0 ~ 1 ~ 1A << 2

• Level 2 >> level 1 because – MANY data products

– Must keep all published

– data Editions (versions)

EOSDIS Core System Information for Scientists, http://observer.gsfc.nasa.gov/sec3/ProductLevels.html

E1

E2

E3E4

time

Level 1A 4 editions of 4 Level 2 products, each is small, but…

http://observer.gsfc.nasa.gov/sec3/ProductLevels.html

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TerraServer http://TerraService.net/

• 3 x 2 TB databases• 18TB disk

tri-plexed (=6TB)

• 3 + 1 Cluster • 99.96% uptime• 1B page views

5B DB queries• Now a .NET

web service

http://terraservice.net/

http://terraservice.net/

http://terraserver.microsoft.com/

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Image Data

USGS Aerial photos “DOQ”USGS Topo Maps

EncartaVirtualGlobe

1 Km resolution

100 % WorldCoverage

• All in the database 200x200 pixel tiles compressed

• Spatial access z-Tranform Btree

12 TB95 % U.S. Coverage

1 m resolution

1 TB100% U.S. Coverage

2 m resolution

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Hardware

SQL\Inst1SQL\Inst1

SQL\Inst2SQL\Inst2

SQL\Inst3SQL\Inst3

SpareSpare

F GLKP Q

E EJ JO O

IHM NR S

22002200 22002200 22002200

220022002200220022002200

22002200 22002200 22002200

One SQL database per rackOne SQL database per rackEach rack contains 4.5 tbEach rack contains 4.5 tb261 total drives / 13.7 TB total261 total drives / 13.7 TB total

Meta DataMeta DataStored on 101 GBStored on 101 GB““Fast, Small Disks”Fast, Small Disks”(18 x 18.2 GB)(18 x 18.2 GB)

Imagery DataImagery DataStored on 4 339 GBStored on 4 339 GB““Slow, Big Disks”Slow, Big Disks”(15 x 73.8 GB)(15 x 73.8 GB)

To Add 90 72.8 GBTo Add 90 72.8 GBDisks in Feb 2001Disks in Feb 2001to create 18 TB SANto create 18 TB SAN

8 Compaq DL360 “Photon” Web Servers8 Compaq DL360 “Photon” Web Servers

Fiber SANFiber SANSwitchesSwitches

4 Compaq ProLiant 8500 Db Servers4 Compaq ProLiant 8500 Db Servers

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TerraServer Lessons Learned• Hardware is 5 9’s (with clustering)• Software is 5 9’s (with clustering)• Admin is 4 9’s (offline maintenance)• Network is 3 9’s (mistakes, environment)

• Simple designs are best• 10 TB DB is management limit

1 PB = 100 x 10 TB DBthis is 100x better than 5 years ago.

• Minimize use of tape–Backup to disk (snapshots)–Portable disk TBs

99 9999 9 9 999 9 999 99

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Sensor Applications• Earth Observation

– 15 PB by 2007• Medical Images & Information + Health

Monitoring– Potential 1 GB/patient/y 1 EB/y

• Video Monitoring– ~1E8 video cameras @ 1E5 MBps

10TB/s 100 EB/y filtered???

• Airplane Engines– 1 GB sensor data/flight, – 100,000 engine hours/day– 30PB/y

• Smart Dust: ?? EB/y

http://robotics.eecs.berkeley.edu/~pister/SmartDust/http://www-bsac.eecs.berkeley.edu/~shollar/macro_motes/macromotes.html

http://robotics.eecs.berkeley.edu/~pister/SmartDust/figures/colormote.gif

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What do they do with the databusiness, government, science

More later in talk• Look for anomalies

– 1, 2, 1, 2, 1, 1, 1, 2, -5, 1, 0, 2,

• Look for trends and patterns– 1, 2, 3, 4, 5,

• Look for correlations– ln(x) – ln(y) ~ c ln(z)

• Look at summaries then drill down to details

• LOTS of histograms

y

x

y

x

ln(z)

Ln(x/y)

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Premise: Grid Computing

• Store exabytes once or twice (for redundancy)

• Access them from anywhere• Implies huge archive/data

centers• Supercomputer centers

become super data centers• Examples:

Google, Yahoo!, Hotmail,CERN, Fermilab, SDSC

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Bandwidth: 3x bandwidth/year for 25 more years

• Today: – 40 Gbps per channel (λ)– 12 channels per fiber (wdm): 500 Gbps– 32 fibers/bundle = 16 Tbps/bundle

• In lab 3 Tbps/fiber (400 x WDM)• In theory 25 Tbps per fiber• 1 Tbps = USA 1996 WAN bisection bandwidth• Aggregate bandwidth doubles every 8 months!

1 fiber = 25 Tbps

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Digital Everything From “words and numbers”From “words and numbers”

to “sights and sounds”to “sights and sounds”

Underlying Theme

New DevicesNew Devices From isolated to adaptive, From isolated to adaptive,

synchronized, and connectedsynchronized, and connected

Automation From dumb to Web services From manual to self-tuning, self

organizing, and self maintaining Beyond reliability to availability

One inter-connected network From stand alone/basic connectivity to

always wired (and wireless) Everything over IP

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Online Science

• All literature online

• All data online

• All instruments online

• Great analysis tools.

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Online Education

• All literature online

• All lectures online

• Interactive and time-shifted education

• Just-in-time education

• Available to everyone everywhere– Economic model is not understood (who pays?)– One model: “society pays”

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Online Business

• Frictionless economy• Near-perfect information• Very efficient• Fully customized products • Example: Wallmart / Dell:

– Traditional business 1-10 inventory turns/y– eBuisiness 100-500 turns/y: no inventory

• VERY efficient, huge economic advantage• Your customers & suppliers loan you money!

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Online Medicine

• Traditional medicine:– Can monitor your health continuously– Instant diagnosis– Personalized drugs

• New Biology– DNA is software– “solve each disease”– Huge impact on agriculture too

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Cyber-Space Shrinks Time and Distance

• Everyone is always connected– Can get information they want – Can communicate with friends & family

• Everything is online– You never miss a meeting/game/party/movie

(you can always watch it)– You never forget anything (its there

somewhere)

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Sustainable Society

• Year 2050: 9 B people living at Europe’s standard of living

• 100M people in a city? • Environment can’t sustain it• More efficient cities/transportation/…• 20% consume 60% now

if 100 % consume 1/3 of current levels net consumption unchanged.

• Need to reduce energy/water/metal consumption 3x in developed world.

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CyberSpace (data) and ToolsCan Augment Human Intelligence

• See next talk (12 CS challenges)

• MyMainBrain is a personal example:improved memory

• Data mining tools are promising

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The End Of Privacy

• You can find out all about me.

• Organizations can precisely track us– Credit cards, email, cellphone, …

• Animals have “tags” in them, I will probably get a tag (eventually)(I already carry a dozen ID & smart cards).

• “You have no privacy, get over it” Scott Mcnealy

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The Centralization of Power• Computers enable an Orwellian future (1984)• The government can know everything you ever

– Buy– Say– Hear– See/Read/…

• Where you are (phone company already knows)

• Who you see and talk to• OK now,

but what if Nero/Hitler/Stalin/.. comes to power?

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Cyber Crime

• You can steal my identity – Sell my house– Accumulate huge debts– Make a video of me doing terrible things.

• You can steal on a grand scale– Now Trillions of dollars are online.– A LARGE honey-pot for criminals.

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Cyber Terrorism

• It is easier to attack/destroy than to steal.

• Viruses, data corruption, data modification

• Denial of Service

• Hijacking and then destroying equipment– Utilities (water, energy, transportation)– Production (factories)

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Monoculture

• Radio & TV & movies & Internetare making the world more homogenous.

• ½ the world has never made a phone call

• But this is changing fast (they want to make phone calls!)

• The wired world enables communities to form very easily – e.g. Sanskrit scholars.

• But the community has to speak a common language.

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Information Clutter

• Most mail is junk mail• Most eMail will soon be junk mail

– 30% of hotmail, 75% of my mail (~130 m/d).

• Telemarketing wastes people’s time.• Creates info-glut• You have 50,000 new mail messages

• Need systems and interfaces to filter, summarize, prioritize information

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Technical Challenges

• Storing information• Organizing information• Summarizing information• Visualizing information• Make programming easy

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The personal Terabyte (all your stuff online)So you’ve got it – now what do you do with it?

• Probably not accessed very often but TREASURED (what’s the one thing you would save in a fire?)

• Can you find anything?• Can you organize that many objects?• Once you find it will you know what it is?• Once you’ve found it, could you find it again?• Research Goal:

Have GOOD answers for all these Questions

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Bell, Gemmell, Lueder: MyLifeBits Guiding Principles

1. Freedom from strict hierarchy– Full text search & Collections

2. Many visualizations– “don’t metaphor me in”

3. Annotations add value– So make them easy!

4. Keep the links when you author– “transclusion”

5. Everything goes in a database

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How will we find it?Put everything in the DB (and index it)

SQLSQL

• Need dbms features Consistency, Indexing, Pivoting, Queries, Speed/scalability, Backup, replicationIf you don’t use one, creating one!

• Simple logical structure: – Blob and link is all that is inherent– Additional properties (facets == extra tables)

and methods on those tables (encapsulation)

• More than a file system • Unifies data and meta-data• Simpler to manage• Easier to subset and reorganize• Set-oriented access• Allows online updates • Automatic indexing, replication

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How do we represent it to the outside world?

• File metaphor too primitive: just a blob• Table metaphor too primitive: just records• Need Metadata describing data context

– Format– Providence (author/publisher/ citations/…)– Rights– History– Related documents

• In a standard format• XML and XML schema• DataSet is great example of this• World is now defining standard schemas

schema

Data ordifgram

<?xml version="1.0" encoding="utf-8" ?>

- <DataSet xmlns="http://WWT.sdss.org/">

- <xs:schema id="radec" xmlns="" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:msdata="urn:schemas-microsoft-com:xml-msdata">

<xs:element name="radec" msdata:IsDataSet="true">

<xs:element name="Table">

<xs:element name="ra" type="xs:double" minOccurs="0" />

<xs:element name="dec" type="xs:double" minOccurs="0" /> …

- <diffgr:diffgram xmlns:msdata="urn:schemas-microsoft-com:xml-msdata" xmlns:diffgr="urn:schemas-microsoft-com:xml-diffgram-v1">

- <radec xmlns="">

- <Table diffgr:id="Table1" msdata:rowOrder="0">

<ra>184.028935351008</ra>

<dec>-1.12590950121524</dec>

</Table>

…

- <Table diffgr:id="Table10" msdata:rowOrder="9">

<ra>184.025719033547</ra>

<dec>-1.21795827920186</dec>

</Table>

</radec>

</diffgr:diffgram>

</DataSet>

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There is a problem

• GREAT!!!!

–XML documents are portable objects

–XML documents are complex objects–WSDL defines the methods on objects (the

class)

• But will all the implementations match?

–Think of UNIX or SQL or C or…

• This is a work in progress.

Niklaus Wirth: Algorithms + Data Structures = Programs

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SQL(for xml)

TemplatesSchema

PhotoServer: Managing Photos

• Load all photos into the database• Annotate the photos• View by various attributes • Do similarity Search• Use XML for interchange• Use dbObject, Template for accessSQL, Templates, XML data

XML datasets & mime data

IIS

DOM

jScript

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How Similarity Search Works• For each picture Loader

– Inserts thumbnails– Extracts 270 Features into a blob

• When looking for similar picture– Scan all photos comparing features

(dot product of vectors)– Sort by similarity

• Feature blob is an array– Today I fake the array with functions and cast

cast(substring(feature,72,8) as float)– When SQL Server gets C# I will not have to fake it. – And… it will run 100x faster (compiled managed code).

• Idea pioneered by IBM Research,we use a variant by MS Beijing Research.

No black squares20% orange

…etc

many black squares10% orange

…etc

72% match 27% match

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Key Observations• Data:

– XML data sets are a universal way to represent answers– XML data sets minimize round trips: 1 request/response

• Search– It is BEST to index– You can put objects and attributes in a row

(SQL puts big blobs off-page)– If you can’t index,

You can extract attributes and quickly compare– SQL can scan at 2M records/cpu/second– Sequential scans are embarrassingly parallel.

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What about Big Data

• Talked about organizing personal data

• What about BIG data.

• Most of the following slides inspired by (or even copied from)

Alex Szalay JHU and

George Djorgovski Cal Tech

56Adapted from slides by Alex Szalay and George Djorgovski

• Exponential growth of data volume, complexity, quality

• But growth SLOW growth of knowledge understanding

• Why?Methodology bottleneck Human wetware limitations …

• Need AI-assisted discovery

Data Knowledge ?

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What’s needed?(not drawn to scale)

Science Data & Questions

Scientists

DatabaseTo store

dataExecuteQueries

Plumbers

Data Mining

Algorithms

Miners

Question & AnswerVisualizat

ion

Tools

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How Discoveries Made?adapted from slide by George Djorgovski

• Conceptual Discoveries: e.g., Relativity, QM, Brane World, Inflation … Theoretical, may be inspired by observations

• Phenomenological Discoveries: e.g., Dark Matter, QSOs, GRBs, CMBR, Extrasolar Planets, Obscured Universe …

Empirical, inspire theories, can be motivated by them

New TechnicalCapabilities

ObservationalDiscoveries

Theory

Phenomenological Discoveries:

Explore parameter space

Make new connections (e.g., multi-)

Understanding of complex phenomena requirescomplex, information-rich data (and simulations?)

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Data Mining in the Image Domain: Can We Discover New Types of Phenomena Using Automated Pattern

Recognition?(Every object detection algorithm has its biases and limitations)

– Effective parametrization of source morphologies and environments– Multiscale analysis (Also: in the time/lightcurve domain)

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Exploration of Parameter Spaces in the Catalog Domain (Source Attributes)

• Clustering Analysis (supervised and unsupervised):

– How many different types of objects are there?– Are there any rare or new types, outliers?

• Multivariate Correlation Search:– Are there significant, nontrivial correlations

present in the data?

Clusters vs. Correlations:

Science Correlations

Correlations reduction ofthe statistical dimensionality

61

New Science from Multivariate Correlations

xi

xj xkf (

x i, x j,

…)

Data dimension DD = 2Statistical dim. DS = 2

DD = 2DS = 1

If DS < DD, thenMV correlationsare present

Correlations objectivelydefine types of objects, e.g.,TFR normal spirals,FP normal ellipticals… and can lead to some new insights

Fundamental Plane of E-galaxies:

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The Curse of Hyper-dimensionality,

But… DD >> 1, DS >> 1Data Complexity Multidimensionality Discoveries

But the bad news is …

The computational cost of clustering analysis:

Some dimensionality reduction methods do exist (e.g., PCA, classprototypes, hierarchical methods, etc.), but more work is needed

Exascale computing and / or better algorithms

K-means: K N I DExpectation Maximization: K N I D2

Monte Carlo Cross-Validation: M Kmax2 N I D2

N = no. of data vectors ~1e12, D = no. of data dimensions ~1e4K = no. of clusters chosen, Kmax = max no. of clusters triedI = no. of iterations, M = no. of Monte Carlo trials/partitions

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The Curse of Hyper-dimensionality

• Visualization!• A fundamental limitation of the human perception:

DMAX = 3? 5? (NB: We can certainly understand mathematically

much higher dimensionalities, but cannot really visualize them; our own Neural Nets are powerful pattern recognition tools)

• Interactive visualization a key part of the data mining process:

• Some methodology exists, but much more is needed

User

DM Algorithm

Visualization

64

Online Multivariate Analysis Challenges

• Data heterogeneity, biases, selection effects …• Non-Gaussianity of clusters (data models)

• Non-trivial topology of clustering

• Useful vs. “useless” parameters …

Outlier population, ora non-Gaussian tail?

65

Useful vs. “Useless” Parameters:Clusters (classes) and correlations may exist/separate

in some parameter subspaces, but not in othersxi

xj

xn

xm

66

Optimal Statisticsfollowing slides adapted from Alex Szalay

• statistics algorithms scale poorly– Correlation functions N2, likelihood techniques N3

• Even if data and computers grow at same rateComputers can do at most N logN algorithms

• Possible solutions?– Assumes infinite computational resources– Assumes that only source of error is statistical– `Cosmic Variance’: we can only observe the Universe

from one location (finite sample size)

• Solutions require combination of Statistics and CS• New algorithms: not worse than N logN

67

Clever Data Structures• Heavy use of tree structures:

– Initial cost NlnN– Large speedup later– Tree-codes for correlations (A. Moore et al 2001)

• Fast, approximate heuristic algorithms– No need to be more accurate than cosmic variance– Fast CMB analysis by Szapudi etal (2001)

• N logN instead of N3 => 1 day instead of 10 million years

• Take cost of computation into account– Controlled level of accuracy– Best result in a given time, given our computing resources

68

Angular Clustering with Photo-z

• w() by Peebles and Groth:– The first example of publishing and analyzing large data

• Samples based on rest-frame quantities

• Strictly volume limited samples

• Largest angular correlation study to date

• Very clear detection of – Luminosity and color dependence

• Results consistent with 3D clustering

T. Budavari, A. Connolly, I. Csabai, I. Szapudi, A. Szalay, S. Dodelson, J. Frieman, R. Scranton, D. Johnston

and the SDSS Collaboration

69

The Samples

343k343k 254k254k 185k185k 316k316k 280k280k 326k326k 185k185k 127k127k

-20 > Mr >-21

1182k1182k

-21 > Mr >-23

931k931k

0.1<z<0.3-20 > Mr

2.2M2.2M

-21 > Mr >-22

662k662k

-22 > Mr >-23

269k269k

0.1<z<0.5-21.4 > Mr

3.1M3.1M

10 stripes: 10M10M

mr<21 : 15M15M

All: 50M50M

2800 square degrees in 10 stripes, data in custom DB

70

The Stripes

• 10 stripes over the SDSS area, covering about 2800 square degrees

• About 20% lost due to bad seeing

• Masks: seeing, bright stars, etc.

• Images generated from query by web service

71

The Masks

• Stripe 11 + masks

• Masks are derived from the database– Search and intersect extended objects with

boundaries

72

The Analysis

• eSpICE : I.Szapudi, S.Colombi and S.Prunet• Integrated with the database by T. Budavari• Extremely fast processing (N logN)

– 1 stripe with about 1 million galaxies is processed in 3 mins

– Usual figure was 10 min for 10,000 galaxies => 70 days

• Each stripe processed separately for each cut• 2D angular correlation function computed• w(): average with rejection of

pixels along the scan– flat field vector causes

mock correlations

73

Angular Correlations I.

• Luminosity dependence: 3 cuts-20> M > -21-21> M > -22-22> M > -23

74

Angular Correlations II.

• Color Dependence4 bins by rest-frame SED type

75

If there’s time

• Better User Interfaces 0 TaskGalary.MPG

• Organizing photos 1 Digital Photo.mpg

• Organizing newsgroups 2 Communities.mpg

• Enhancing meetings. 3 flows.mpg

• Attentional interfaces: 4 Side Show.mpg

76

Thesis

• Most new information is digital(and old information is being digitized)

• A Computer Science Grand Challenge:– Capture– Organize– Summarize– Visualize

This information• Optimize Human Attention as a resource.• Improve information quality

77






Documents

The Information Avalanche: Reducing Information Overload