Transcript - Microsoft Research

Online Science
the New Computational Science
Jim Gray
Microsoft Research
http://research.microsoft.com/~gray
Alex Szalay
Johns Hopkins
1
Outline
• The Evolution of X-Info – how CS can help
• The World Wide Telescope as Archetype
• How I work with them: a case study
Experiments &
Instruments
Other Archives facts
Literature
facts
questions
?
answers
Simulations
The Big Problems
•
•
•
•
•
•
Data ingest
Managing a petabyte
Common schema
How to organize it
How to reorganize it
How to coexist with others
•
•
•
•
Query and Vis tools
Integrating data and Literature
Support/training
Performance
– Execute queries in a minute
– Batch query scheduling
2
Evolving Science
• Empirical Science
– Scientist gathers data by direct observation
– Scientist analyzes data
• Analytical Science
– Scientist builds analytical model
– Makes predictions.
2
 .
4G
c2
a
 a   3   a 2
 
• Computational Science
– Simulate analytical model
– Validate model and makes predictions
• Science - Informatics
– Data captured by instruments
Or data generated by simulator
– Processed by software
– Placed in a database / files
– Scientist analyzes database / files
3
Information Avalanche
• In science, industry, government,….
– better observational instruments and
– and, better simulations
producing a data avalanche
Image courtesy
C. Meneveau & A. Szalay @ JHU
• Examples
– BaBar: Grows 1TB/day
2/3 simulation Information
1/3 observational Information
– CERN: LHC will generate 1GB/s .~10 PB/y
– VLBA (NRAO) generates 1GB/s today
– Pixar: 100 TB/Movie
BaBar, Stanford
P&E Gene Sequencer From
http://www.genome.uci.edu/
• New emphasis on informatics:
– Capturing, Organizing,
Summarizing, Analyzing, Visualizing
4
Space Telescope
The Virtual Observatory
• Premise: most data is (or could be online)
• 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
– It’s a smart telescope:
links objects and data to literature
• Software is the capital expense
– Share, standardize, reuse..
5
Why Is Astronomy Special?
• Almost all literature online and public
ADS:
http://adswww.harvard.edu/
CDS:
http://cdsweb.u-strasbg.fr/
• Data has no commercial value
IRAS 25m
2MASS 2m
– No privacy concerns, freely share results with others
DSS Optica
– Great for experimenting with algorithms
• It is real and well documented
– High-dimensional
– Spatial, temporal
(with confidence intervals)
IRAS 100m
• Diverse and distributed
– Many different instruments from
many different places and
many different times
WENSS 92cm
NVSS 20cm
• The community wants to share the data
• There is a lot of it (soon petabytes)
6
ROSAT ~keV
GB 6cm
Time and Spectral Dimensions
The Multiwavelength Crab Nebulae
Crab star
1053 AD
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.
7
Slide courtesy of Robert Brunner @ CalTech.
Global Federations
• Massive datasets live near their owners:
– Near the instrument’s software pipeline
– Near the applications
– Near data knowledge and curation
• 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
Federation
8
Data Access Hitting a Wall
Current science practice based on data download
(FTP/GREP)
Will not scale to the datasets of tomorrow
•
•
•
•
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$)
… 2 days and 1K$
… 3 years and 1M$
• Oh!, and 1PB ~5,000 disks
• At some point you need
indices to limit search
parallel data search and analysis
• This is where databases can help
9
Experiment Budgets ¼…½ Software
Software for
• Instrument scheduling
• Instrument control
• Data gathering
• Data reduction
• Database
• Analysis
• Visualization
Millions of lines of code
Repeated for experiment
after experiment
Not much sharing or learning
Let’s work to change this
Identify generic tools
• Workflow schedulers
• Databases and libraries
• Analysis packages
• Visualizers
10
• …
What’s X-info Needs from us (cs)
(not drawn to scale)
Miners
Scientists
Science Data
& Questions
Data Mining
Algorithms
Plumbers
Database
To store data
Execute
Queries
Question &
Answer
Visualization
Tools
11
Web Services: Enable Federation
• Web SERVER:
– Given a url + parameters
– Returns a web page (often dynamic)
Your
program
Web
Server
• 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
• Now: Find object models
for each science.
Your
program
Data
In your
address
space
Web
Service
12
New Approaches to Data Analysis
• Looking for
– Needles in haystacks – the Higgs particle
– Haystacks: Dark matter, Dark energy
• Needles are easier than haystacks
• Global statistics have poor scaling
– Correlation functions are N2, likelihood techniques N3
• As data and computers grow at same rate,
we can only keep up with N logN
• A way out?
– Discard notion of optimal (data is fuzzy, answers are approximate)
– Don’t assume infinite computational resources or memory
• Requires combination of statistics & computer science
From Alex Szalay
13
Analysis and Databases
• Much statistical analysis deals with
–
–
–
–
–
–
–
–
–
Creating uniform samples –
data filtering
Assembling relevant subsets
Estimating completeness
Censoring bad data
Counting and building histograms
Generating Monte-Carlo subsets
Likelihood calculations
Hypothesis testing
• Traditionally these are performed on files
• Most of these tasks are much better done inside a database
• Move Mohamed to the mountain, not the mountain to Mohamed.
From Alex Szalay
14
SkyServer.SDSS.org
• A modern archive
– Access to Sloan Digital Sky Survey
Spectroscopic and Optical surveys
– Raw Pixel data lives in file servers
– Catalog data (derived objects) lives in Database
– Online query to any and all
• Also used for education
– 150 hours of online Astronomy
– Implicitly teaches data analysis
• Interesting things
–
–
–
–
–
Spatial data search
Client query interface via Java Applet
Query from Emacs, Python, ….
Cloned by other surveys (a template design)
Web services are core of it.
15
SkyQuery (http://skyquery.net/)
• Distributed Query tool using a set of web services
• Many astronomy archives from
Pasadena, Chicago, Baltimore, Cambridge (England)
• Has grown from 4 to 15 archives,
now becoming
international standard
• WebService Poster Child
• Allows queries like:
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.type=3 and (o.I - t.m_j)>2
16
SkyQuery Structure
• Each SkyNode publishes
– Schema Web Service
– Database Web Service
• Portal is
– Plans Query (2 phase)
– Integrates answers
– Is itself a web service
Image
Cutout
SDSS
INT
SkyQuery
Portal
FIRST
2MASS
17
Outline
• The Evolution of X-Info – how CS can help
• The World Wide Telescope as Archetype
• How I work with them: a case study
Experiments &
Instruments
Other Archives facts
Literature
facts
questions
?
answers
Simulations
The Big Problems
•
•
•
•
•
•
Data ingest
Managing a petabyte
Common schema
How to organize it
How to reorganize it
How to coexist with others
•
•
•
•
Query and Vis tools
Integrating data and Literature
Support/training
Performance
– Execute queries in a minute
– Batch query scheduling
21
How to Help?
• Can’t learn the discipline before you start
(takes 4 years.)
• Can’t go native – you are a CS person
not a bio,… person
• Have to learn how to communicate
Have to learn the language
• Have to form a working relationship with
domain expert(s)
• Have to find problems that leverage your skills
22
Working Cross-Culture
How to Design the Database:
Scenario Design
• 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 utility procedures
– JHU Built Query GUI for Linux /Mac/.. clients
23
The 20 Queries
Q11: 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
Also some good queries at:
count of galaxies within 30"of it that have a photoz24
within
http://www.sdss.jhu.edu/ScienceArchive/sxqt/sxQT/Example_Queries.html
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.)
Two kinds of SDSS data in an SQL DB
(objects and images all in DB)
• 100M Photo Objects ~ 400 attributes
400K
Spectra
with
~30 lines/
spectrum
25
An easy one: Q7:
Provide a list of star-like objects that are 1% rare.
• Found 14,681 buckets,
first 140 buckets have 99%
time 104 seconds
• Disk bound, reads 3 disks at 68 MBps.
Select cast((u-g) as int) as ug,
cast((g-r) as int) as gr,
cast((r-i) as int) as ri,
cast((i-z) as int) as iz,
count(*)
as Population
from stars
group by
cast((u-g) as int), cast((g-r) as int),
cast((r-i) as int), cast((i-z) as int)
order by count(*)
26
Then What?
1999. 20 Queries were a way to engage
–
–
Needed spatial data library
Needed DB design
2000. Built website to publish the data
2001. Data Loading (workflow scheduler).
2002. Pixel web service that evolved…
2003. SkyQuery federation evolved…
2004. Now focused on spatial data library.
Conversion to SQL 2005 (put analysis in DB).
27
Alternate Model
• Many sciences are becoming
information sciences
• Modeling systems
needs new and better languages.
• CS modeling tools can help
– Bio, Eco, Linguistic, …
• This is the process/program centric view
rather than my info-centric view.
28
Outline
• The Evolution of X-Info – how CS can help
• The World Wide Telescope as Archetype
• How I work with them: a case study
Experiments &
Instruments
Other Archives facts
Literature
facts
questions
?
answers
Simulations
The Big Problems
•
•
•
•
•
•
Data ingest
Managing a petabyte
Common schema
How to organize it
How to reorganize it
How to coexist with others
•
•
•
•
Query and Vis tools
Integrating data and Literature
Support/training
Performance
– Execute queries in a minute
– Batch query scheduling
29
Call to Action
• X-info is emerging.
• Computer Scientists can help in many ways.
– Tools
– Concepts
– Provide technology consulting to the commuity
• There are great CS research problems here
– Modeling
– Analysis
– Visualization
– Architecture
30
References
•
•
•
http://SkyServer.SDSS.org/
http://research.microsoft.com/pubs/
http://research.microsoft.com/Gray/SDSS/ (download personal SkyServer)
Data Mining the SDSS SkyServer Database
Jim Gray; Peter Kunszt; Donald Slutz; Alex Szalay; Ani Thakar; Jan Vandenberg; Chris Stoughton Jan. 2002 40 p.
An earlier paper described the Sloan Digital Sky Survey’s (SDSS) data management needs [Szalay1] by defining twenty database queries and twelve data
visualization tasks that a good data management system should support. We built a database and interfaces to support both the query load and also a
website for ad-hoc access. This paper reports on the database design, describes the data loading pipeline, and reports on the query implementation and
performance. The queries typically translated to a single SQL statement. Most queries run in less than 20 seconds, allowing scientists to interactively
explore the database. This paper is an in-depth tour of those queries. Readers should first have studied the companion overview paper “The SDSS
SkyServer – Public Access to the Sloan Digital Sky Server Data” [Szalay2].
SDSS SkyServer–Public Access to Sloan Digital Sky Server Data
Jim Gray; Alexander Szalay; Ani Thakar; Peter Z. Zunszt; Tanu Malik; Jordan Raddick; Christopher Stoughton; Jan Vandenberg November 2001 11 p.:
Word 1.46 Mbytes PDF 456 Kbytes The SkyServer provides Internet access to the public Sloan Digital Sky Survey (SDSS) data for both astronomers and
for science education. This paper describes the SkyServer goals and architecture. It also describes our experience operating the SkyServer on the
Internet. The SDSS data is public and well-documented so it makes a good test platform for research on database algorithms and performance.
•
The World-Wide Telescope
•
Jim Gray; Alexander Szalay August 2001 6 p.: Word 684 Kbytes PDF 84 Kbytes
All astronomy data and literature will soon be online and accessible via the Internet. The community is building the Virtual Observatory, an organization of this worldwide data into a coherent
whole that can be accessed by anyone, in any form, from anywhere. The resulting system will dramatically improve our ability to do multi-spectral and temporal studies that integrate data from
multiple instruments. The virtual observatory data also provides a wonderful base for teaching astronomy, scientific discovery, and computational science.
•
Designing and Mining Multi-Terabyte Astronomy Archives
•
Robert J. Brunner; Jim Gray; Peter Kunszt; Donald Slutz; Alexander S. Szalay; Ani Thakar
June 1999 8 p.: Word (448 Kybtes) PDF (391 Kbytes)
The next-generation astronomy digital archives will cover most of the sky at fine resolution in many wavelengths, from X-rays, through ultraviolet, optical, and infrared. The archives will be
stored at diverse geographical locations. One of the first of these projects, the Sloan Digital Sky Survey (SDSS) is creating a 5-wavelength catalog over 10,000 square degrees of the sky (see
http://www.sdss.org/). The 200 million objects in the multi-terabyte database will have mostly numerical attributes in a 100+ dimensional space. Points in this space have highly correlated
distributions.
•
There Goes the Neighborhood: Relational Algebra for Spatial Data Search,
•
Extending the SDSS Batch Query System to the National Virtual Observatory Grid,
•
with Alexander S. Szalay, Gyorgy Fekete, Wil O’Mullane, Aniruddha R. Thakar, Gerd Heber, Arnold H. Rots, MSR-TR-2004-32,
Maria A. Nieto-Santisteban, William O'Mullane, Jim Gray, Nolan Li, Tamas Budavari, Alexander S. Szalay, Aniruddha R. Thakar, MSR-TR-2004-12.
Explains how the astronomers are building personal databases and a simple query scheduler into their astronomy data-grid portals.
31
Outline
• New Science
• Working cross disciplines
• Data Demographics and Data Handling
– Exponential growth
– Data Lifecycle
– Versions
– Data inflation
– Year 5
– Overprovision by 6x
Experiments &
Instruments
– Data Loading
– Regression Tests
Other Archives facts
facts
– Statistical subset
Literature
• Curation
?
Simulations
questions
answers
32
Information Avalanche
• In science, industry, government,….
– better observational instruments and
– and, better simulations
producing a data avalanche
Image courtesy
C. Meneveau & A. Szalay @ JHU
• Examples
– BaBar: Grows 1TB/day
2/3 simulation Information
1/3 observational Information
– CERN: LHC will generate 1GB/s .~10 PB/y
– VLBA (NRAO) generates 1GB/s today
– Pixar: 100 TB/Movie
BaBar, Stanford
P&E Gene Sequencer From
http://www.genome.uci.edu/
• New emphasis on informatics:
– Capturing, Organizing,
Summarizing, Analyzing, Visualizing
33
Space Telescope
Q: Where will the Data Come From?
A: 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
34
Instruments: CERN – LHC
Peta Bytes per Year
Looking for the Higgs Particle
• Sensors:
~1 GB/s (~ 20 PB/y)
• Events
100 MB/s
• Filtered
10 MB/s
• Reduced
1 MB/s
CERN Tier 0
Data pyramid:
100GB : 1TB : 100TB : 1PB : 10PB
35
•
Like all sciences,
Astronomy Faces an Information
Avalanche
Astronomers have a few hundred TB now
– 1 pixel (byte) / sq arc second ~ 4TB
– Multi-spectral, temporal, … → 1PB
• They mine it looking for
1000
100
new (kinds of) objects or
more of interesting ones (quasars),
density variations in 400-D space
correlations in 400-D space
•
•
•
•
Data doubles every year
Data is public after 1 year
So, 50% of the data is public
Same access for everyone
10
1
0.1
1970
1975
1980
1985
1990
1995
2000
CCDs
Glass
36
Moore’s Law in Proteomics
Courtesy of Peter Berndt, Roche Center for Medical Genomics (RCMG)
Roche Center for Medical Genomics (RCMG):
number of mass-spectra acquired for proteomics
doubled every year since first mass spectrometer
deployed.
Count of Spectra
Proteomics MS Data Generation
500000.
R2=0.96
200000.
100000.
50000
20000
1998
1999
2000
2001
2002
Year
37
2003
Data Lifecycle
• Raw data → primary data → derived data
• Data has bugs:
– Instrument bugs
– Pipeline bugs
• Data comes in versions
– later versions fix known bugs
– Just like software (indeed data is software)
• Can’t “un-publish” bad data.
Level 1
calibrated
Level 0
raw
instrument
or
simulator
pipeline
pipeline
other
data
Level 2
derived
other
data
38
Data Inflation – Data Pyramid
Level 2
Level 1A
Grows X TB/year
~ .4X TB/y
compressed
(level 1A in NASA terms)
Derived data products ~10x smaller
But there are many.
L2≈L1
• Publish new edition each year
– Fixes bugs in data.
– Must preserve old editions
– Creates data pyramid
• Store each edition
– 1, 2, 3, 4… N ~ N2 bytes
• Net: Data Inflation: L2 ≥ L1
Level 1A
4 editions of 4 Level 2 products
E4
E3
time
E2
E1
4 editions of
level 1A data
(source data)
4 editions of level 2 derived data products. Note that each derived product is
small, but they are numerous. This proliferation combined with the data
pyramid implies that level2 data more than doubles the total storage volume.
39
180
The Year 5 Problem
Yearly Demand
160
Depreciated Inflated Demand
• Data arrives at R bytes/year
• New Storage & Processing
– Need to buy R units in year N
• Data inflation means
Yearly Demand ( R )
140
80
60
40
20
0
– Need to buy NR units
0
• Capital expense
peaks at year 5
• See 6x Over-Power slide next
4
6
8
10
8
10
Yearly Capital Cost
4.0
3.5
Marginal Capital Cost
60%/year price decline
2
Year
• Depreciate over 3 years
• Moore’s law:
Naive Demand
100
~N2R
– After year 3
need to buy N2R + (N-3)2R
Inflated Demand
120
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
2
4
6
Year
40
6x Over-Power Ratio
• If you think you need X raw capacity,
then you probably need 6X
• Reprocessing
• Backup copies
• Versions
• …
• Hardware is cheap,
Your time is precious.
PubDB
3.6TB
DR2C
1.8TB
DR2M
1.8TB
DR2P
1.8TB
DR3C
2.4TB
DR3M
2.4TB
DR3P
2.4TB
41
Data Loading
• Data from outside
– Is full of bugs
– Is not in your format
• Advice
– Get it in a “Universal Format”
(e.g. Unicode CSV)
– Create Blood-Brain barrier
Quarantine in a “load database”
– Scrub the data
•
•
•
•
Cross check everything you can
Check data statistics for sanity
Reject or repair bad data
Generate detailed bug reports
(needed to send rejection upstream)
– Expect to reload many times
Automate everything!
LOAD
Export
EXP
Check CSV
CHK Build Task DBs
BLD Build SQL Schema
SQL Validate
VAL
Backup
BCK
Detach
DTC
PUBLISH
Publish
PUB
Cleanup
CLN
Test
Test Uniqueness
Uniqueness
Of
Of Primary
Primary Keys
Keys
FINISH
FIN
Test the unique
Key in each table
Test
Test
Foreign
Foreign Keys
Keys
Test for consistency
of keys that link tables
Test
Test
Cardinalities
Cardinalities
Test consistency of
numbers of various
quantities
Test
Test
HTM
HTM IDs
IDs
Test
Test parent-child
parent-child
consistency
consistency
Test the Hierarchical
Triamgular Mesh IDs
used for spatial
indexing
42
Ensure that all parents
and children and linked
Performance Prediction &
Regression
• Database grows exponentially
• Set up response-time requirements
– For load
– For access
• Define a workload to measure each
• Run it regularly to detect anomalies
• SDSS uses
– one-week to reload
– 20 queries with response of 10 sec to 10 min.
43
Data Subsets
For Science and Development
• Offer 1GB, 10GB, …, Full
subsets
• Wonderful tool for you
– Debug algorithms
• Good tool for scientists
– Experiment on subset
– Not for needle in haystack,
but good for global stats
• Challenge: How make
statistically valid subsets?
– Seems domain specific
– Seems problem specific
– But, must be some general
concepts.
44
Outline
•
•
•
•
New Science
Working cross disciplines
Data Demographics and Data Handling
Curation
– Problem statement
– Economics
– Astro as a case in point
Experiments &
Instruments
Other Archives facts
Literature
Simulations
facts
?
questions
answers
45
Problem Statement
• Once published,
scientific data needs to be available forever,
so that the science can be reproduced/extended.
• What does that mean?
NASA “level 0”
– Data can be characterized as
• Primary Data: could not be reproduced
• Derived data: could be derived from primary data.
– Meta-data: how the data was collected/derived
is primary
• Must be preserved
• Includes design docs, software, email, pubs, personal
notes, teleconferences,
46
The Core Problem: No Economic
Model
• The archive user is not yet born.
How can he pay you to curate the data?
• The Scientist gathered data for his own purpose
Why should he pay (invest time) for your needs?
• Answer to both: that’s the scientific method
• Curating data
(documenting the design, the acquisition and the processing)
Is difficult and there is little reward for doing it.
Results are rewarded, not the process of getting them.
• Storage/archive NOT the problem (it’s almost free)
• Curating/Publishing is expensive,
MAKE IT EASIER!!! (lower the cost)
47
Publishing Data
Roles
Traditional
Emerging
Authors
Scientists
Collaborations
Publishers
Journals
Project web site
Curators
Libraries
Data+Doc Archives
Archives
Archives
Digital Archives
Consumers Scientists
Scientists
48
Changing Roles
• Exponential growth:
–
–
–
–
Projects last at least 3-5 years
Project data online during project lifetime.
Data sent to central archive only at the end of the project
At any instant, only 1/8 of data is in central archives
• New project responsibilities
– Becoming Publishers and Curators
– Larger fraction of budget spent on software
• Standards are needed
– Easier data interchange, fewer tools
• Templates are needed
– Much development duplicated, wasted
49
What SDSS is Doing: Capture the Bits
(preserve the primary data)
• Best-effort documenting data and process
Documents and data are hyperlinked.
• Publishing data: often by UPS
(~ 5TB today and so ~5k$ for a copy)
• Replicating data on 3 continents.
• EVERYTHING online (tape data is dead data)
• Archiving all email, discussions, ….
• Keeping all web-logs & query logs.
• Now we need to figure out how to
50
organize/search all this metadata.
Schema (aka metadata)
• Everyone starts with the same schema
<stuff/>
Then the start arguing about semantics.
• Virtual Observatory: http://www.ivoa.net/
• Metadata based on Dublin Core:
http://www.ivoa.net/Documents/latest/RM.html
• Universal Content Descriptors (UCD):
http://vizier.u-strasbg.fr/doc/UCD.htx
Captures quantitative concepts and their units
Reduced from ~100,000 tables in literature to ~1,000 terms
• VOtable – a schema for answers to questions
http://www.us-vo.org/VOTable/
• Common Queries:
Cone Search and Simple Image Access Protocol, SQL
• Registry: http://www.ivoa.net/Documents/latest/RMExp.html
still a work in progress.
51
Summary
• New Science
– X-Info for all fields X
– WWT as an example
– Big Picture
– Puzzle
– Hitting the wall
– Needle in haystack
– Move queries to data
• Working cross disciplines
– How to help?
– 20 questions
– WWT example
– Alt: CS Process Models
• Data Demographics
– Exponential growth
– Data Lifecycle
– Versions
– Data inflation
– Year 5 is peak cost
– Overprovision by 6x
– Data Loading
– Regression Tests
– Statistical subset
• Curation
– Problem statement
– Economics
– Astro as a case in point
52
Call to Action
• X-info is emerging.
• Computer Scientists can help in many ways.
– Tools
– Concepts
– Provide technology consulting to the community
• There are great CS research problems here
– Modeling
– Analysis
– Visualization
– Architecture
53
References
http://SkyServer.SDSS.org/
http://research.microsoft.com/pubs/
http://research.microsoft.com/Gray/SDSS/ (download personal SkyServer)
Extending the SDSS Batch Query System to the National Virtual Observatory Grid,
M. A. Nieto-Santisteban, W. O'Mullane, J. Gray, N. Li, T. Budavari, A. S. Szalay, A. R. Thakar, MSR-TR-2004-12, Feb. 2004
Scientific Data Federation,
J. Gray, A. S. Szalay, The Grid 2: Blueprint for a New Computing Infrastructure, I. Foster, C. Kesselman, eds, Morgan Kauffman,
2003, pp 95-108.
Data Mining the SDSS SkyServer Database,
J. Gray, A.S. Szalay, A. Thakar, P. Kunszt, C. Stoughton, D. Slutz, J. vandenBerg, Distributed Data & Structures 4:
Records of the 4th International Meeting, pp 189-210, W. Litwin, G. Levy (eds),, Carleton Scientific 2003, ISBN 1-894145-13-5,
also MSR-TR-2002-01, Jan. 2002
Petabyte Scale Data Mining: Dream or Reality?,
Alexander S. Szalay; Jim Gray; Jan vandenBerg, SIPE Astronomy Telescopes and Instruments, 22-28 August 2002, Waikoloa,
Hawaii, MSR-TR-2002-84
Online Scientific Data Curation, Publication, and Archiving,
J. Gray; A. S. Szalay; A.R. Thakar; C. Stoughton; J. vandenBerg, SPIE Astronomy Telescopes and Instruments, 22-28 August
2002, Waikoloa, Hawaii, MSR-TR-2002-74
The World Wide Telescope: An Archetype for Online Science,
J. Gray; A. Szalay,, CACM, Vol. 45, No. 11, pp 50-54, Nov. 2002, MSR TR 2002-75,
The SDSS SkyServer: Public Access To The Sloan Digital Sky Server Data,
A. S. Szalay, J. Gray, A. Thakar, P. Z. Kunszt, T. Malik, J. Raddick, C. Stoughton, J. vandenBerg:,
ACM SIGMOD 2002: 570-581 MSR TR 2001 104.
The World Wide Telescope,
A.S., Szalay, J., Gray, Science, V.293 pp. 2037-2038. 14 Sept 2001. MS-TR-2001-77
Designing & Mining Multi-Terabyte Astronomy Archives: Sloan Digital Sky Survey,
A. Szalay, P. Kunszt, A. Thakar, J. Gray, D. Slutz, P. Kuntz, June 1999, ACM SIGMOD 2000, MS-TR-99-30,
54