Jim Gray, Online Science: Onassis Foundation Science Lecture Series, FORTH, Heraklion, Crete, Greece, 17 July 2002 1 1 Online Science -- The World-Wide

Jim Gray, Online Science: Onassis Foundation Science Lecture Series, FORTH, Heraklion, Crete, Greece, 17 July 20021

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Online Science -- The World-Wide Telescope Archetype

Jim Gray

Microsoft Research

Onassis Foundation Science Lecture Series

17 July 2002, FORTH, Heraklion, Crete

In Collaboration with:

Alex Szalay,… @ JHU

Robert Brunner, Roy Williams, George Djorgovski @ Caltech


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Outline

• The revolution in Computational Science

• The Virtual Observatory Concept

== World-Wide Telescope

• The Sloan Digital Sky Survey & DB technology


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Computational Science The Third Science Branch is Evolving

• In the beginning science was empirical.• Then theoretical branches evolved.

• Now, we have computational branches.– Was primarily simulation– Growth areas:

data analysis & visualization of peta-scale instrument data.

• Help both simulation and instruments.

• Are primitive today.


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Computational Science

• Traditional Empirical Science – Scientist gathers data by direct

observation– Scientist analyzes data

• Computational Science– Data captured by instruments

Or data generated by simulator– Processed by software– Placed in a database / files– Scientist analyzes database / files


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What Do Scientists Do With The Data?They Explore Parameter Space

• There is LOTS of data – people cannot examine most of it.– Need computers to do analysis.

• Manual or Automatic Exploration– Manual: person suggests hypothesis,

computer checks hypothesis– Automatic: Computer suggests hypothesis

person evaluates significance

• Given an arbitrary parameter space:– Data Clusters– Points between Data Clusters– Isolated Data Clusters– Isolated Data Groups– Holes in Data Clusters– Isolated Points

Nichol et al. 2001Slide courtesy of and adapted from Robert Brunner @ CalTech.


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Challenge to Data Miners: Rediscover Astronomy

• Astronomy needs deep understanding of physics.

• But, some was discovered as variable correlations then “explained” with physics.

• Famous example: Hertzsprung-Russell Diagramstar luminosity vs color (=temperature)

• Challenge 1 (the student test): How much of astronomy can data mining discover?

• Challenge 2 (the Turing test):Can data mining discover NEW correlations?


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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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Data MiningScience vs Commerce

• Data in files FTP a local copy /subset.ASCII or Binary.

• Each scientist builds own analysis toolkit

• Analysis is tcl script of toolkit on local data.

• Some simple visualization tools: x vs y

• Data in a database

• Standard reports for standard things.

• Report writers for non-standard things

• GUI tools to explore data.– Decision trees– Clustering– Anomaly finders


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But…some science is hitting a wallFTP and GREP are not adequate

• You can GREP 1 MB in a second• You can GREP 1 GB in a minute • You can GREP 1 TB in 2 days• You can GREP 1 PB in 3 years.

• Oh!, and 1PB ~10,000 disks

• At some point you need indices to limit searchparallel data search and analysis

• This is where databases can help

• You can FTP 1 MB in 1 sec• You can FTP 1 GB / min (= 1 $/GB)

• … 2 days and 1K$• … 3 years and 1M$


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Goal: Easy Data Publication & Access

• Augment FTP with data query: Return intelligent data subsets

• Make it easy to – Publish: Record structured data– Find:

• Find data anywhere in the network• Get the subset you need

– Explore datasets interactively

• Realistic goal: – Make it as easy as

publishing/reading web sites today.


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Web Services: The Key?• Web SERVER:

– Given a url + parameters

– Returns a web page (often dynamic)

• Web SERVICE:– Given a XML document (soap msg)

– Returns an XML document

– Tools make this look like an RPC.• F(x,y,z) returns (u, v, w)

– Distributed objects for the web.

– + naming, discovery, security,..

• Internet-scale distributed computing

Yourprogram

DataIn your address

space

Web Service

soap

object

in

xml

Yourprogram Web

Server

http

Web

page


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Federation

Data Federations of Web Services• Massive datasets live near their owners:

– Near the instrument’s software pipeline

– Near the applications

– Near data knowledge and curation

– Super Computer centers become Super Data Centers

• Each Archive publishes a web service– Schema: documents the data– Methods on objects (queries)

• Scientists get “personalized” extracts

• Uniform access to multiple Archives– A common global schema


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Grid and Web Services Synergy• I believe the Grid will be many web services• IETF standards Provide

– Naming– Authorization / Security / Privacy– Distributed Objects

Discovery, Definition, Invocation, Object Model

– Higher level services: workflow, transactions, DB,..

• Synergy: commercial Internet & Grid tools


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Outline






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Why Astronomy Data?•It has no commercial value

–No privacy concerns–Can freely share results with others–Great for experimenting with algorithms

•It is real and well documented–High-dimensional data (with confidence intervals)–Spatial data–Temporal data

•Many different instruments from many different places and many different times

•Federation is a goal•The questions are interesting

–How did the universe form?

•There is a lot of it (petabytes)

IRAS 100

ROSAT ~keV

DSS Optical

2MASS 2

IRAS 25

NVSS 20cm

WENSS 92cm

GB 6cm


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Time and Spectral DimensionsThe Multiwavelength Crab Nebulae

X-ray, optical,

infrared, and radio

views of the nearby Crab

Nebula, which is now in a state of

chaotic expansion after a supernova

explosion first sighted in 1054 A.D. by Chinese Astronomers.

Crab star 1053 AD

Graphic courtesy of Robert Brunner @ CalTech.


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Even in “optical” images are very differentOptical Near-Infrared Galaxy Image Mosaics

BJ RF IN J H KBJ RF IN J H K

Graphic courtesy of Robert Brunner @ CalTech.

One object in 6 different

“color” bands


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Astronomy Data Growth• In the “old days” astronomers took photos.

• Starting in the 1960’s they began to digitize.• New instruments are digital (100s of GB/nite)

• Detectors are following Moore’s law.

• Data avalanche: double every 2 years

Growth over 25 years is a factor of 30 in glass,a factor of 3000 in pixels.

Graphic

Courtesy of

Alex Szalay

Total area of world’s 3m+ telescopes (m2)

Total number of CCD pixels (megapixel)


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Universal Access to Astronomy Data • Astronomers have a few Petabytes now.

– 1 pixel (byte) / sq arc second ~ 4TB– Multi-spectral, temporal, … → 1PB

• They mine it looking for new (kinds of) objects or more interesting ones (quasars), density variations in 400-D space correlations in 400-D space

• Data doubles every 2 years.• Data is public after 2 years.• So, 50% of the data is public.• Some have private access to 5% more data.• So: 50% vs 55% access for everyone


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The Age of Mega-Surveys• Large number of new surveys

– multi-TB in size, 100 million objects or more

– Data publication an integral part of the survey

– Software bill a major cost in the survey

• These mega-surveys are different– top-down design

– large sky coverage

– sound statistical plans

– well controlled/documented data processing

• Each survey has a publication plan

• Federating these archives

Virtual Observatory

MACHO2MASSDENISSDSSPRIMEDPOSSGSC-IICOBE MAPNVSSFIRSTGALEXROSATOGLE LSST...

MACHO2MASSDENISSDSSPRIMEDPOSSGSC-IICOBE MAPNVSSFIRSTGALEXROSATOGLE LSST...


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Data Publishing and Access• But…..• How do I get at that 50% of the data?• Astronomers have culture of publishing.

– FITS files and many tools.http://fits.gsfc.nasa.gov/fits_home.html

– Encouraged by NASA.– FTP what you need.

• But, data “details” are hard to document. Astronomers want to do it, but it is VERY difficult.(What programs where used? What were the processing steps? How were errors treated?…)

• And by the way, few astronomers have a spare petabyte of storage in their pocket.

• THESIS:Challenging problems are

publishing dataproviding good query & visualization tools

http://fits.gsfc.nasa.gov/fits_home.html


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Virtual Observatoryhttp://www.astro.caltech.edu/nvoconf/

http://www.voforum.org/

• Premise: Most data is (or could be online)• So, the Internet is the world’s best telescope:

– It has data on every part of the sky– In every measured spectral band: optical, x-ray, radio..

– As deep as the best instruments (2 years ago).

– It is up when you are up.The “seeing” is always great (no working at night, no clouds no moons no..).

– It’s a smart telescope: links objects and data to literature on them.


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Sky Server: A Part of the VO http://skyserver.sdss.org/

• Originally designed for high school students– Contains 150 hours of interactive courses

• Now provides easy science access to SDSS data– Professionals– Armatures– Students

• The Virtual Observatory can teach – Astronomy:

make it interactive, show ideas and phenomena

– Computational science real data & real problems


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Virtual Observatory Challenges• Size : multi-Petabyte

40,000 square degrees is 2 Trillion pixels– One band (at 1 sq arcsec) 4 Terabytes– Multi-wavelength 10-100 Terabytes– Time dimension >> 10 Petabytes– Need auto parallelism tools

• Unsolved MetaData problem– Hard to publish data & programs– How to federate Archives– Hard to find/understand data & programs

• Current tools inadequate– new analysis & visualization tools– Data Federation is problematic

• Transition to the new astronomy


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Current Status• Defining Astronomy Objects and Methods.• Federated 3 Web Services (fermilab/sdss, jhu/first, Cal Tech/dposs)

Does multi-survey cross-correlation and selection Distributed query optimization (T. Malik, T. Budavari, Alex Szalay @ JHU)

http://skyquery.net/

• Cutout + annotated SDSS images web service– http://SkyService.jhu.pha.edu/SdssCutout

• WWT is a great Web Services application– Federating heterogeneous data sources.– Cooperating organizations– An Information At Your Fingertips challenge.


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Outline






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Sloan Digital Sky Survey http://www.sdss.org/

• For the last 12 years a group of astronomers has been building a telescope (with funding from Sloan Foundation, NSF, and a dozen universities). 90M$.

• Y2000: engineer, calibrate, commission: now public data.– 5% of the survey, 600 sq degrees, 15 M objects 60GB, ½ TB raw.

– This data includes most of the known high z quasars.

– It has a lot of science left in it but….

• New the data is arriving: – 250GB/nite (20 nights per year) = 5TB/y.

– 100 M stars, 100 M galaxies, 1 M spectra.

http://www.sdss.org/news/features/20001005.ded.html


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Q: How can a computer scientist help, without learning a LOT of Astronomy?A: Scenario Design: 20 questions.

• Astronomers proposed 20 questions Typical of things they want to do Each would require a week of programming in tcl / C++/ FTP

• Goal, make it easy to answer questions

• DB and tools design motivated by this goal– Implemented DB & utility procedures– JHU Built GUI for Linux clients


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The 20 QueriesQ11: Find all elliptical galaxies with spectra that have an

anomalous emission line. Q12: Create a grided count of galaxies with u-g>1 and r<21.5 over

60<declination<70, and 200<right ascension<210, on a grid of 2’, and create a map of masks over the same grid.

Q13: Create a count of galaxies for each of the HTM triangles which satisfy a certain color cut, like 0.7u-0.5g-0.2i<1.25 && r<21.75, output it in a form adequate for visualization.

Q14: Find stars with multiple measurements and have magnitude variations >0.1. Scan for stars that have a secondary object (observed at a different time) and compare their magnitudes.

Q15: Provide a list of moving objects consistent with an asteroid.Q16: Find all objects similar to the colors of a quasar at

5.5<redshift<6.5.Q17: Find binary stars where at least one of them has the colors of a

white dwarf.Q18: Find all objects within 30 arcseconds of one another that have

very similar colors: that is where the color ratios u-g, g-r, r-I are less than 0.05m.

Q19: Find quasars with a broad absorption line in their spectra and at least one galaxy within 10 arcseconds. Return both the quasars and the galaxies.

Q20: For each galaxy in the BCG data set (brightest color galaxy), in 160<right ascension<170, -25<declination<35 count of galaxies within 30"of it that have a photoz within 0.05 of that galaxy.

Q1: Find all galaxies without unsaturated pixels within 1' of a given point of ra=75.327, dec=21.023

Q2: Find all galaxies with blue surface brightness between and 23 and 25 mag per square arcseconds, and -10<super galactic latitude (sgb) <10, and declination less than zero.

Q3: Find all galaxies brighter than magnitude 22, where the local extinction is >0.75.

Q4: Find galaxies with an isophotal surface brightness (SB) larger than 24 in the red band, with an ellipticity>0.5, and with the major axis of the ellipse having a declination of between 30” and 60”arc seconds.

Q5: Find all galaxies with a deVaucouleours profile (r¼ falloff of intensity on disk) and the photometric colors consistent with an elliptical galaxy. The deVaucouleours profile

Q6: Find galaxies that are blended with a star, output the deblended galaxy magnitudes.

Q7: Provide a list of star-like objects that are 1% rare.Q8: Find all objects with unclassified spectra. Q9: Find quasars with a line width >2000 km/s and

2.5<redshift<2.7. Q10: Find galaxies with spectra that have an equivalent width in Ha

>40Å (Ha is the main hydrogen spectral line.)

Also some good queries at: http://www.sdss.jhu.edu/ScienceArchive/sxqt/sxQT/Example_Queries.html

http://www.sdss.jhu.edu/ScienceArchive/sxqt/sxQT/Example_Queries.html


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Two kinds of SDSS data in an SQL DB(objects and images all in DB)

• 15M Photo Objects ~ 400 attributes

50K Spectra with ~30 lines/spectrum


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An Easy QueryQ15: Find asteroids

• Sounds hard but there are 5 pictures of the object at 5 different times (color filters) and so can “see” velocity.

• Image pipeline computes velocity.• Computing it from the 5 color x,y would also be fast• Finds 1,303 objects in 3 minutes, 140MBps.

(could go 2x faster with more disks)

select objId, dbo.fGetUrlEq(ra,dec) as url --return object ID & url sqrt(power(rowv,2)+power(colv,2)) as velocity from photoObj -- check each object.where (power(rowv,2) + power(colv, 2)) -- square of velocity

between 50 and 1000 -- huge values =error


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Q15: Fast Moving Objects• Find near earth asteroids:

• Finds 3 objects in 11 minutes– (or 52 seconds with an index)

• Ugly, but consider the alternatives (c programs an files and…)

–

SELECT r.objID as rId, g.objId as gId, dbo.fGetUrlEq(g.ra, g.dec) as url

FROM PhotoObj r, PhotoObj gWHERE r.run = g.run and r.camcol=g.camcol

and abs(g.field-r.field)<2 -- nearby-- the red selection criteriaand ((power(r.q_r,2) + power(r.u_r,2)) > 0.111111 )and r.fiberMag_r between 6 and 22 and r.fiberMag_r < r.fiberMag_g and r.fiberMag_r < r.fiberMag_iand r.parentID=0 and r.fiberMag_r < r.fiberMag_u and r.fiberMag_r < r.fiberMag_zand r.isoA_r/r.isoB_r > 1.5 and r.isoA_r>2.0-- the green selection criteriaand ((power(g.q_g,2) + power(g.u_g,2)) > 0.111111 )and g.fiberMag_g between 6 and 22 and g.fiberMag_g < g.fiberMag_r and g.fiberMag_g < g.fiberMag_iand g.fiberMag_g < g.fiberMag_u and g.fiberMag_g < g.fiberMag_zand g.parentID=0 and g.isoA_g/g.isoB_g > 1.5 and g.isoA_g > 2.0-- the matchup of the pairand sqrt(power(r.cx -g.cx,2)+ power(r.cy-g.cy,2)+power(r.cz-g.cz,2))*(10800/PI())< 4.0and abs(r.fiberMag_r-g.fiberMag_g)< 2.0


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Performance (on current SDSS data)

time vs queryID

1

10

100

1000

Q08 Q01 Q09 Q10A Q19 Q12 Q10 Q20 Q16 Q02 Q13 Q04 Q06 Q11 Q15B Q17 Q07 Q14 Q15A Q05 Q03 Q18

seco

nd

s cpu

elapsedae

• All queries have fairly short SQL programs -- a substantial advance over tcl, C++

• Run times (2 cpu, 1 GB , 8 disk)

• Some take 10 minutes

• Some take 1 minute

• Median ~ 22 sec.


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Outline






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Call to Action

• If you do data visualization: we need you(and we know it).

• If you do databases:here is some data you can practice on.

• If you do distributed systems:here is a federation you can practice on.

• If you do data mininghere are datasets to test your algorithms.

• If you do astronomy educational outreachhere is a tool for you.

• The astronomers are very good, and very smart, and a pleasure to work with, and the questions are cosmic, so …


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VO & SkyServer references

• NVO Science Definition (an NSF report) http://www.nvosdt.org/• VO Forum website http://www.voforum.org/• World-Wide Telescope Science, V.293 pp. 2037-2038. 14 Sept 2001. word or pdf.)

• Data Mining the SDSS SkyServer DatabaseJim Gray; Peter Kunszt; Donald Slutz; Alex Szalay; Ani Thakar; Jan Vandenberg; Chris Stoughton Jan. 2002 40 p.

• SDSS SkyServer–Public Access to Sloan Digital Sky Server DataGray, Szalay, Thakar, Kunszt, Malik, Raddick, Stoughton, Vandenberg November 2001 Word PDF

• Designing and Mining Multi-Terabyte Astronomy Archives Gray, Kunszt; Slutz, Szalay, Thakar, June 1999 Word PDF http://SkyServer.SDSS.org/http://research.microsoft.com/pubs/


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Documents

Jim Gray, Online Science: Onassis Foundation Science Lecture Series, FORTH, Heraklion, Crete, Greece, 17 July 2002 1 1 Online Science -- The World-Wide