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Online Science -The World-Wide Telescope Archetype
Jim Gray
Microsoft Research
Collaborating with:
Alex Szalay, Ani Thakar,… @ JHU
Roy Williams, George Djorgovski, Julian Bunn @ Caltech
Robert Brunner @ U.I.
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Outline
• The revolution in Computational Science
• The Virtual Observatory Concept
== World-Wide Telescope
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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:
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–
–
–
–
–
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Data Clusters
Points between Data Clusters
Isolated Data Clusters
Isolated Data Groups
Holes in Data Clusters
Isolated Points
Points / clusters similar to “this one”
Nichol et al. 52001
Slide courtesy of and adapted from Robert Brunner @ CalTech.
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 Diagram
star 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)
Miners
Scientists
Science Data
& Questions
Data Mining
Algorithms
Plumbers
Database
To store data
Execute
Queries
Question &
Answer
Visualization
Tools
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Some science is hitting a wall
FTP 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.
•
•
•
•
You can FTP 1 MB in 1 sec
You can FTP 1 GB / min (= 1 $/GB)
…
2 days and 1K$
…
3 years and 1M$
• Oh!, and 1PB ~3,000 disks
• At some point you need
indices to limit search
parallel data search and analysis
• This is where databases can help
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The Digital Shoebox
Personal
• In the old days
people took photos
had them developed
put them in a shoe box
• Some people actually put
them in picture albums.
• But mostly, pictures are
never seen again
it is hard to find anything
Science
• In the old days scientists
kept notebooks.
• Now they keep ftp servers
• Some put them in indexed
databases
• But mostly, data are never
seen again and it is hard to
find anything.
How do we find data subsets in the shoebox?
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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)
Your
program
Web
Server
• Web SERVICE:
– Given a url + 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
Your
program
Data
In your
address
space
Web
Service
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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
• The revolution in Computational Science
• The Virtual Observatory Concept
== World-Wide Telescope
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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
• Federation: Uniform access to multiple Archives
– A common global schema
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Why Astronomy Data?
IRAS 25m
•It has no commercial value
–No privacy concerns
–Can freely share results with others
–Great for experimenting with algorithms
2MASS 2m
•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
IRAS 100m
WENSS 92cm
NVSS 20cm
–How did the universe form?
•There is a lot of it (petabytes)
DSS Optical
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ROSAT ~keV
GB 6cm
Astronomy Data Growth
•
•
•
•
•
•
In the “old days” astronomers took photos.
Now instruments are digital (100s of GB/nite)
Detectors are following Moore’s law.
Data avalanche: double every 2 years
all data more than 2 years old is public
About 1 PB public now
1000.00
100.00
Courtesy
of
Alex
Szalay
10.00
Total area of world’s
3m+ telescopes (m2)
Total number of CCD
pixels (megapixel)
1.00
Glass
1970
1975
1980
1985
1990
CCDs
1995
0.10
2000
Growth over 25 years is
a factor of 30 in glass,
a factor of 3000 in pixels
.
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Time and Spectral Dimensions
The Multiwavelength Crab Nebulae
Crab star
1053 AD
X-ray, optical, infrared, and
radio views of the Crab
Nebula, which is now
chaotically expanding after
a supernova sighted in 1054
A.D. by Chinese
Astronomers.
Szalay’s variant of Metcalf’s Law:
The utility of N different data sets is approxmately N2/2
Each pair of comparisons gives additional information.
The Federation value is superlinear in size.
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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
MACHO
2MASS
DENIS
SDSS
PRIME
DPOSS
GSC-II
COBE
MAP
NVSS
FIRST
GALEX
ROSAT
OGLE
LSST...
• Federating these archives
Slide courtesy of Alex18Szalay,
 Virtual Observatory
modified by Jim
Data Publishing and Access
• But…..
• How do I get at that petabyte of public 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 (today).
• THESIS:
Challenging problems are
publishing data
providing good query & visualization tools
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Virtual Observatory
http://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:
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–
–
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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
• Alex Szalay of Johns Hopkins builSkyServer
(based on TerraServer design) http://skyserver.sdss.org/
• Data access & Astronomy education
• ~7M web hits, usage growing 15%/month
• Moving to V4 DB & Schema (1.5 TB DB + 5TB image by 7/1/2003)
• Recent CS efforts have been
–
–
automated data pipeline (workflow engine) and
web services integration with VO
• Template widely used and cloned in the
Astronomy and Computer Science communities
• Prototype for publishing an Astronomy archive on web.
300 M Photo Objects ~ 400 attributes
1M
Spectra with
~30 lines/
spectrum
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Virtual Observatory Status
• Lots of meetings (too many)
• VO table defined (a successor to FITS?)
– Tool suite emerging
• Defining Astronomy Objects and Methods.
• Federated 5 Web Services (fermilab/sdss, jhu/first, Cal Tech/dposs, Cambrige/nt)
– http://skyquery.net/ multi-survey crossID match and select
Distributed query optimization
– http://SkyService.jhu.pha.edu/SdssCutout Image access service (cutout + annotated)
• WWT is a great Web Services (.Net) application
– Federating heterogeneous data sources.
– Cooperating organizations
– An Information At Your Fingertips challenge.
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SkyQuery Web Services
http://skyquery.net/
Basic Services
• Metadata about resources
– Waveband
– Sky coverage
– Translation of names to universal
dictionary (UCD)
• Simple search resources
– Cone Search
– Image mosaic
– Unit conversions
• Filtering, counting, histograms
• On-the-fly recalibrations
Higher Level Services
• Built on Atomic Services
• Perform more complex tasks
• Examples
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Automated resource discovery
Cross-identifications
Photometric redshifts
Outlier detections
Visualization facilities
• Goal:
– Build custom portals in days
from existing building blocks
(like today in IRAF or IDL)
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SkyQuery Cross-id Steps
•
•
•
•
•
http://skyquery.net/
Parse query
Get counts
Sort by counts
Make plan
Cross-match
– Recursively,
from small to large
SELECT o.objId, o.r,
o.type, t.objId
FROM SDSS:PhotoPrimary o,
TWOMASS:PhotoPrimary t
WHERE XMATCH(o,t)<3.5
AND AREA(181.3,-0.76,6.5)
AND (o.i - t.m_j) > 2
AND o.type=3
• Select necessary attributes only
• Return output
• Insert cutout image
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Summary
• The revolution in Computational Science
simulation & analysis
• The Virtual Observatory Concept
== World-Wide Telescope
• I finally found a distributed database
• I have found a
distributed system and a
distributed object system.
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References
NVO (Virtual Observatory)
WWT (world wide telescope)
• NVO Science Definition (an NSF report)
http://www.nvosdt.org/
• VO Forum website http://www.voforum.org/
• World-Wide Telescope paper in Science
V.293 pp. 2037-2038. 14 Sept 2001. (MS-TR-2001-77 word or pdf.)
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