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Title: eScience: TheNext Decade WillBe Exciting Talk Johns Hopkins University, Computer Science, 23 Februar


1
eScience The Next Decade Will Be ExcitingTalk
_at_ Johns Hopkins University, Computer Science,23
February 2006
  • Jim Gray Alex Szalay
  • Microsoft Research Johns Hopkins University
  • Gray_at_Microsoft.com Szalay_at_pha.JHU.edu
  • http//research.microsoft.com/gr
    ay/talks

2
eScience  The Next Decade Will Be Exciting.
  • All scientific data and literature is coming
    online and will be cross-indexed.
  • Funding agencies are forcing the scientific
    literature into the public domain. Scientific
    data, traditionally horded by investigators (with
    notable exceptions), will also become public. 
  • The forced electronic publication of scientific
    literature and data poses some deep technical
    questions just exactly how does anyone read and
    understand it now and a century from now?
  • Each intellectual discipline X is building an
    X-informatics and computational-X
    branch. Progress has been astonishing, but the
    real changes will happen in the next decade.
  • The X-info branches, in collaboration with
    computer science, must cooperate to solve these
    problems. Ive been pursuing these questions in
    Geography (with http//TerraService.Net),
    Astronomy (with the World-Wide telescope -- e.g.
    http//SkyServer.Sdss.org and http//www.ivoa.net/
    ) and more recently in bio informatics (with
    portable PubMedCentral).

3
Outline
  • The Evolution of X-Info
  • Online Literature
  • Online Data
  • The World Wide Telescope as Archetype

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

4
Science Paradigms
  • Thousand years ago science was empirical
  • describing natural phenomena
  • Last few hundred years theoretical branch
  • using models, generalizations
  • Last few decades a computational branch
  • simulating complex phenomena
  • Today data exploration (eScience)
  • unify theory, experiment, and simulation
  • using data management and statistics
  • Data captured by instrumentsOr generated by
    simulator
  • Processed by software
  • Scientist analyzes database / files

5
Computational Science Evolves
Image courtesy C. Meneveau A. Szalay _at_ JHU
  • Historically, Computational Science simulation.
  • New emphasis on informatics
  • Capturing,
  • Organizing,
  • Summarizing,
  • Analyzing,
  • Visualizing
  • Largely driven by observational science, but
    also needed by simulations.
  • Will comp-X and X-info will unify or compete?

PE Gene Sequencer From http//www.genome.uci.edu/
BaBar, Stanford
Space Telescope
6
What X-info Needs from us (cs)(not drawn to
scale)
7
Experiment Budgets ¼½ Software
  • Millions of lines of code
  • Repeated for experiment after experiment
  • Not much sharing or learning
  • Lets work to change this
  • Identify generic tools
  • Workflow schedulers
  • Databases and libraries
  • Analysis packages
  • Visualizers
  • Software for
  • Instrument scheduling
  • Instrument control
  • Data gathering
  • Data reduction
  • Database
  • Analysis
  • Visualization

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.
  • 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
  • You can FTP 1 MB in 1 sec
  • You can FTP 1 GB / min (1)
  • 2 days and 1K
  • 3 years and 1M

9
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)
  • Dont assume infinite computational resources or
    memory
  • Requires combination of statistics computer
    science

10
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.

11
Extensible Databases
  • Things added to DB (using procedures)
  • temporal and spatial indexing
  • Clever data structures (trees, cubes)
  • Large creation cost, but logN access cost
  • Tree-codes for correlations (A. Moore et al 2001)
  • Datacubes for OLAP (all vendors)
  • Fast, approximate heuristic algorithms
  • No need to be more accurate than data variance
  • Fast CMB analysis by Szapudi etal (2001)N logN
    instead of N3 gt 1 day instead of 10 million
    years
  • Easy to reorganize the data
  • Multiple views, each optimal for certain types of
    analyses
  • Building hierarchical summaries are trivial
  • Automatic parallelism (cps, disks, )
  • Scalable to Petabyte datasets

12
Outline
  • The Evolution of X-Info
  • Online Literature
  • Online Data
  • The World Wide Telescope as Archetype

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

13
And it Is Coming Online
  • Agencies and Foundations mandating research be
    public domain.
  • NIH (30 B/y, 40k PIs,)(see http//www.taxpayera
    ccess.org/)
  • Welcome Trust
  • Japan, China, Italy, South Africa,.
  • Public Library of Science..
  • Other agencies will follow NIH
  • Publishers will resist (not surprising)
  • Professional societies will resist (amazing!)

14
How Does the New Library Work?
  • Who pays for storage access? (unfunded mandate).
  • Its cheap 1 milli-dollar per access
  • But curation is not cheap
  • Author/Title/Subject/Citation/..
  • Dublin Core is great but
  • NLM has a 6,000-line XSD for documents
    http//dtd.nlm.nih.gov/publishing
  • Need to capture document structure from author
  • Sections, figures, equations, citations,
  • Automate curation
  • NCBI-PubMedCentral is doing this
  • Preparing for 1M articles/year
  • MUST be automatic.

15
The OAIS model (open archive information system)
Data Management
Producer
Ingest
Archive
Access
Consumer
Administer
16
Ingest Challenges
  • Push vs Pull
  • What are the representation gold standards?
  • Auto-Migration (Format conversion)
  • Automatic indexing, annotation, provenance.
  • Version management
  • How capture time varying sources
  • Capture dark matter (encapsulated data)
  • Bits dont rust but applications do.

17
Jims Model of Library Science ?
  • Alexandria
  • Gutenberg
  • (Melvil) Dewey Decimal
  • MARC (Henriette Avram)
  • Dublin Core
  • NLM DTD

Yes, I know there have been other things.
18
MetaData Dublin Core
  • Elements
  • Title
  • Creator
  • Subject
  • Description
  • Publisher
  • Contributor
  • Date
  • Type
  • Format
  • Identifier
  • Source
  • Language
  • Coverage
  • Rights
  • Elements
  • Audience
  • Alternative
  • TableOfContents
  • Abstract
  • Created
  • Valid
  • Available
  • Issued
  • Modified
  • Extent
  • Medium
  • IsVersionOf
  • HasVersion
  • IsReplacedBy
  • Replaces
  • IsRequiredBy
  • Requires
  • IsPartOf
  • Encoding
  • LCSH (Lb. Congress Subject Head)
  • MESH (Medical Subject Head)
  • DDC (Dewey Decimal Classification)
  • LCC (Lb. Congress Classification)
  • UDC (Universal Decimal Classification)
  • DCMItype (Dublin Core Meta Type)
  • IMT (Internet Media Type)
  • ISO639-2 (ISO language names)
  • RFC1766 (Internet Language tags)
  • URI (Uniform Resource Locator)
  • Point (DCMI spatial point)
  • ISO3166 (ISO country codes)
  • Box (DCMI rectangular area)
  • TGN (Getty Thesaurus of Geo Names)
  • Period (DCMI time interval)
  • W3CDTF (W3C date/time)
  • RFC3066 (Language dialects)

19
Access Challenges
  • Archived information rusts if it is not
    accessed. Access is essential.
  • Access costs money who pays?
  • Access sometimes uses IP, who pays?
  • There are also technical problems
  • Access formats different from the storage
    formats.
  • migration?
  • emulation?
  • Gold Standards?

20
Archive Challenges
  • Cost of administering storage
  • Presently 10x to 100x the hardware cost.
  • Resist attack geographic diversity
  • At 1GBps it takes 12 days to move a PB
  • Store it in two (or more) places online (on
    disk). A geo-plex
  • Scrub it continuously (look for errors)
  • On failure,
  • use other copy until failure repaired,
  • refresh lost copy from safe copy.
  • Can organize the copies differently (e.g.
    one by time, one by space)

21
Tangible Things (1)
  • Information at your fingertips
  • Helping build PortablePubMedCentral
  • Deployed US, China, England, Italy, South Africa,
    (Japan soon).
  • Each site can accept documents
  • Archives replicated
  • Federate thru web services
  • Working to integrate Word/Excel/ with
    PubmedCentral e.g. WordML, XSD,
  • To be clear NCBI is doing 99 of the work.

22
Tangible Things (2)
  • Currently support a conference peer-review
    system (300 conferences)
  • Form committee
  • Accept Manuscripts
  • Declare interest/recuse
  • Review
  • Decide
  • Form program
  • Notify
  • Revise

23
Tangible Things (2)
  • Add publishing steps
  • Form committee
  • Accept Manuscripts
  • Declare interest/recuse
  • Review
  • Decide
  • Form program
  • Notify
  • Revise
  • Publish
  • Connect to Archives
  • Manage archive document versions
  • Capture Workshop
  • presentations
  • proceedings
  • Capture classroom ConferenceXP
  • Moderated discussions of published articles

24
Why Not a Wicki?
  • Peer-Review is
  • It is very structured
  • It is moderated
  • There is a degree of confidentiality
  • Wicki is egalitarian
  • Its a conversation
  • Its completely transparent
  • Dont get me wrong
  • Wickis are great
  • SharePoints are great
  • But.. Peer-Review is different.
  • And, incidentally review of proposals,
    projects, is more like peer-review.

25
Why Am I Telling You This?
  • Library Science has challenging problems
    (not all of them are social/economic).
  • Library Science is central to the way we do
    science
  • Teaching research
  • Review evaluation
  • Search access
  • Increasingly Library Science
    is Computer Science
  • Its Info-Info in the X-info model
  • Its not just search.

26
Outline
  • The Evolution of X-Info
  • Online Literature
  • Online Data
  • The World Wide Telescope as Archetype

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

27
So What about Publishing Data?
  • The answer is 42.
  • But
  • What are the units?
  • How precise? How accurate 42.5 .01
  • Show your work data provenance

28
Publishing Data
  • Exponential growth
  • Projects last at least 3-5 years
  • Data sent to deep archive at project end
  • Data will never be centralized
  • More responsibility on projects
  • Becoming Publishers and Curators
  • Often no explicit funding to do this (must
    change)
  • Data will reside with projects
  • Analyses must be close to the data (see later)
  • Data cross-correlated with Literature and Metadata

29
Data Curation Problem Statement
  • Once published, scientific data needs to be
    available forever,so that the science can be
    reproduced/extended.
  • What does that mean?
  • 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/derivedis
    primary
  • Must be preserved
  • Includes design docs, software, email, pubs,
    personal notes, teleconferences,

NASA level 0
30
Thought Experiment
  • You have collected some dataand want to publish
    science based on it.
  • How do you publish the data so that others can
    read it and reproduce your results in 100
    years?
  • Document collection process?
  • How document data processing (scrubbing
    reducing the data)?
  • Where do you put it?

31
The Vision Global Data Federation
  • Massive datasets live near their owners
  • Near the instruments 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
32
The Best Example Entrez-GenBankhttp//www.ncbi.n
lm.nih.gov/
  • Sequence data deposited with Genbank
  • Literature references Genbank ID
  • BLAST searches Genbank
  • Entrez integrates and searches
  • PubMedCentral
  • PubChem
  • Genbank
  • Proteins, SNP,
  • Structure,..
  • Taxononomy

33
The Midrange Paradox
  • Large archives are curated by projects
  • Small archives (appendices) curated by journals
  • Medium-sized archives are in limbo
  • No place to register them
  • No one has mandate to preserve them
  • Examples
  • Your website with your data files
  • Small scale science projects
  • Genbank gets the sequence but not the software
    or analysis that produced it.

34
Objectifying Knowledge
  • This requires agreement about
  • Units cgs
  • Measurements who/what/when/where/how
  • CONCEPTS
  • Whats a planet, star, galaxy,?
  • Whats a gene, protein, pathway?
  • Need to objectify science
  • what are the objects?
  • what are the attributes?
  • What are the methods (in the OO sense)?
  • This is mostly Physics/Bio/Eco/Econ/... But CS
    can do generic things

35
Objectifying Knowledge
  • This requires agreement about
  • Units cgs
  • Measurements who/what/when/where/how
  • CONCEPTS
  • Whats a planet, star, galaxy,?
  • Whats a gene, protein, pathway?
  • Need to objectify science
  • what are the objects?
  • what are the attributes?
  • What are the methods (in the OO sense)?
  • This is mostly Physics/Bio/Eco/Econ/... But CS
    can do generic things

Warning!Painful discussions ahead The O
word Ontology The S word Schema The CV
words Controlled Vocabulary Domain experts
do not agree
36
Web Services Enable Federation
  • 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
  • Now Find object modelsfor each science.

37
Outline
  • The Evolution of X-Info
  • Online Literature
  • Online Data
  • The World Wide Telescope as Archetype

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

38
World Wide TelescopeVirtual Observatoryhttp//w
ww.us-vo.org/
http//www.ivoa.net/
  • Premise Most data is (or could be online)
  • So, the Internet is the worlds 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..).
  • Its a smart telescope links objects and
    data to literature on them.

39
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
  • There is a lot of it (petabytes)

40
Time and Spectral DimensionsThe 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.
Slide courtesy of Robert Brunner _at_ CalTech.
41
SkyServer.SDSS.org
  • A modern archive
  • Access to Sloan Digital Sky SurveySpectroscopic
    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.

42
SkyServerSkyServer.SDSS.org
  • Like the TerraServer, but looking the other way
    a picture of ¼ of the universe
  • Sloan Digital Sky Survey Data Pixels Data
    Mining
  • About 400 attributes per object
  • Spectrograms for 1 of objects

43
Demo of SkyServer
  • Shows standard web server
  • Pixel/image data
  • Point and click
  • Explore one object
  • Explore sets of objects (data mining)

44
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
SDSSPhotoPrimary o, TWOMASSPhotoPrimary t
WHERE XMATCH(o,t)lt3.5 AND AREA(181.3,-0.76,6.5)
AND o.type3 and (o.I - t.m_j)gt2
45
SkyQuery Structure
  • Each SkyNode publishes
  • Schema Web Service
  • Database Web Service
  • Portal is
  • Plans Query (2 phase)
  • Integrates answers
  • Is itself a web service

46
SkyNode Basic Web Services
  • Metadata information about resources
  • Waveband
  • Sky coverage
  • Translation of names to universal dictionary
    (UCD)
  • Simple search patterns on the resources
  • Cone Search
  • Image mosaic
  • Unit conversions
  • Simple filtering, counting, histogramming
  • On-the-fly recalibrations

47
Portals Higher Level Services
  • Built on Atomic Services
  • Perform more complex tasks
  • Examples
  • 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)

48
SkyServer/SkyQuery Evolution MyDB and Batch Jobs
  • Problem need multi-step data analysis (not just
    single query).
  • Solution Allow personal databases on portal
  • Problem some queries are monsters
  • Solution Batch schedule on portal. Deposits
    answer in personal database.

49
Outline
  • The Evolution of X-Info
  • Online Literature
  • Online Data
  • The World Wide Telescope as Archetype

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

50
How to Help?
  • Cant learn the discipline before you
    start(takes 4 years.)
  • Cant go native you are a CS person not a
    bio, person
  • Have to learn how to communicateHave to learn
    the language
  • Have to form a working relationship with domain
    expert(s)
  • Have to find problems that leverage your skills

51
Working Cross-Culture How to Design the
DatabaseScenario 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

52
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-ggt1
    and rlt21.5 over 60ltdeclinationlt70, and 200ltright
    ascensionlt210, 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-0.2ilt1.25 rlt21.75, output it in
    a form adequate for visualization.
  • Q14 Find stars with multiple measurements and
    have magnitude variations gt0.1. 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 5.5ltredshiftlt6.5.
  • 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 160ltright
    ascensionlt170, -25ltdeclinationlt35 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 ra75.327,
    dec21.023
  • Q2 Find all galaxies with blue surface
    brightness between and 23 and 25 mag per square
    arcseconds, and -10ltsuper galactic latitude (sgb)
    lt10, and declination less than zero.
  • Q3 Find all galaxies brighter than magnitude 22,
    where the local extinction is gt0.75.
  • Q4 Find galaxies with an isophotal surface
    brightness (SB) larger than 24 in the red band,
    with an ellipticitygt0.5, and with the major axis
    of the ellipse having a declination of between
    30 and 60arc 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 gt2000 km/s and
    2.5ltredshiftlt2.7.
  • Q10 Find galaxies with spectra that have an
    equivalent width in Ha gt40Å (Ha is the main
    hydrogen spectral line.)

Also some good queries at http//www.sdss.jhu.edu
/ScienceArchive/sxqt/sxQT/Example_Queries.html
53
Two kinds of SDSS data in an SQL DB(objects and
images all in DB)
  • 300M Photo Objects 400 attributes

800K Spectra with 30 lines/ spectrum
54
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 1GBps.

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()
55
An easy one Q15 Provide a list of moving
objects consistent with an asteroid.
  • Sounds hard but there are 5 pictures of the
    object at 5 different times (colors) and so can
    compute velocity.
  • Image pipeline computes velocity.
  • Computing it from the 5 color x,y would also be
    fast
  • Finds 285 objects in 3 minutes, 140MBps.

select objId, -- return object ID
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
56
Q15 Fast Moving Objects
  • Find near earth asteroids
  • SELECT r.objID as rId, g.objId as gId, r.run,
    r.camcol, r.field as field, g.field as gField,
  • r.ra as ra_r, r.dec as dec_r, g.ra as ra_g,
    g.dec as dec_g,
  • sqrt( power(r.cx -g.cx,2) power(r.cy-g.cy,2)pow
    er(r.cz-g.cz,2) )(10800/PI()) as distance
  • FROM PhotoObj r, PhotoObj g
  • WHERE
  • r.run g.run and r.camcolg.camcol and
    abs(g.field-r.field)lt2 -- the match criteria
  • -- the red selection criteria
  • and ((power(r.q_r,2) power(r.u_r,2)) gt
    0.111111 )
  • and r.fiberMag_r between 6 and 22 and
    r.fiberMag_r lt r.fiberMag_g and r.fiberMag_r lt
    r.fiberMag_i
  • and r.parentID0 and r.fiberMag_r lt r.fiberMag_u
    and r.fiberMag_r lt r.fiberMag_z
  • and r.isoA_r/r.isoB_r gt 1.5 and r.isoA_rgt2.0
  • -- the green selection criteria
  • and ((power(g.q_g,2) power(g.u_g,2)) gt
    0.111111 )
  • and g.fiberMag_g between 6 and 22 and
    g.fiberMag_g lt g.fiberMag_r and g.fiberMag_g lt
    g.fiberMag_i
  • and g.fiberMag_g lt g.fiberMag_u and g.fiberMag_g
    lt g.fiberMag_z
  • and g.parentID0 and g.isoA_g/g.isoB_g gt 1.5 and
    g.isoA_g gt 2.0
  • -- the matchup of the pair
  • and sqrt(power(r.cx -g.cx,2) power(r.cy-g.cy,2)
    power(r.cz-g.cz,2))(10800/PI())lt 4.0

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58
A Hard One Q14 Find stars with multiple
measurements that have magnitude variations
gt0.1.
  • This should work, but SQL Server does not allow
    table values to be piped to table-valued
    functions.
  • This should work, but SQL Server does not allow
    table values to be piped to table-valued
    functions.

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A Hard one Second Try Q14Find stars with
multiple measurements that have magnitude
variations gt0.1.
  • Write a program with a cursor, ran for 2 days

--------------------------------------------------
----------------------------- -- Table-valued
function that returns the binary stars within a
certain radius -- of another (in arc-minutes)
(typically 5 arc seconds). -- Returns the ID
pairs and the distance between them (in
arcseconds). create function BinaryStars(_at_MaxDista
nceArcMins float) returns _at_BinaryCandidatesTable
table( S1_object_ID bigint not null, -- Star
1 S2_object_ID bigint not null, -- Star
2 distance_arcSec float) -- distance between
them as begin declare _at_star_ID bigint,
_at_binary_ID bigint-- Star's ID and binary ID
declare _at_ra float, _at_dec float -- Star's
position declare _at_u float, _at_g float, _at_r float,
_at_i float,_at_z float -- Star's colors  
----------------Open a cursor over stars and get
position and colors declare star_cursor cursor
for select object_ID, ra, dec, u, g, r, i,
z from Stars open star_cursor   while
(11) -- for each star begin -- get its
attribues fetch next from star_cursor into
_at_star_ID, _at_ra, _at_dec, _at_u, _at_g, _at_r, _at_i, _at_z if
(_at__at_fetch_status -1) break -- end if no more
stars insert into _at_BinaryCandidatesTable --
insert its binaries select _at_star_ID,
S1.object_ID, -- return stars pairs
sqrt(N.DotProd)/PI()10800 -- and distance in
arc-seconds from getNearbyObjEq(_at_ra, _at_dec,
-- Find objects nearby S. _at_MaxDistanceArcMins)
as N, -- call them N. Stars as S1 --
S1 gets N's color values where _at_star_ID lt
N.Object_ID -- S1 different from S and
N.objType dbo.PhotoType('Star') -- S1 is a
star and N.object_ID S1.object_ID -- join
stars to get colors of S1N and
(abs(_at_u-S1.u) gt 0.1 -- one of the colors is
different. or abs(_at_g-S1.g) gt 0.1 or
abs(_at_r-S1.r) gt 0.1 or abs(_at_i-S1.i) gt 0.1
or abs(_at_z-S1.z) gt 0.1 ) end -- end
of loop over all stars -------------- Looped
over all stars, close cursor and exit. close
star_cursor -- deallocate star_cursor
return -- return table end -- end of
BinaryStars GO select from dbo.BinaryStars(.05)
60
A Hard one Third TryQ14 Find stars with
multiple measurements that have magnitude
variations gt0.1.
  • Use pre-computed neighbors table.
  • Ran in 17 minutes, found 31k pairs.


-- Plan 2 Use
the precomputed neighbors table select top 100
S.object_ID, S1.object_ID, -- return star pairs
and distance str(N.Distance_mins 60,6,1) as
DistArcSec from Stars S, -- S is a
star Neighbors N, -- N within 3 arcsec (10
pixels) of S. Stars S1 -- S1 N has the
color attibutes where S.Object_ID
N.Object_ID -- connect S and N. and
S.Object_ID lt N.Neighbor_Object_ID -- S1
different from S and N.Neighbor_objType
dbo.PhotoType('Star')-- S1 is a star (an
optimization) and N.Distance_mins lt .05 --
the 3 arcsecond test and N.Neighbor_object_ID
S1.Object_ID -- N S1 and (
abs(S.u-S1.u) gt 0.1 -- one of the colors is
different. or abs(S.g-S1.g) gt 0.1 or
abs(S.r-S1.r) gt 0.1 or abs(S.i-S1.i) gt 0.1 or
abs(S.z-S1.z) gt 0.1 ) -- Found 31,355 pairs
(out of 4.4 m stars) in 17 min 14 sec.
61
The Pain of Going Outside SQL(its fortunate that
all the queries are single statements)
  • Use a cursor
  • No cpu parallelism
  • CPU bound
  • 6 MBps, 2.7 k rps
  • 5,450 seconds (10x slower)
  • Count parent objects
  • 503 seconds for 14.7 M objects in 33.3 GB
  • 66 MBps
  • IO bound (30 of one cpu)
  • 100 k records/cpu sec

declare _at_count int declare _at_sum int set _at_sum
0 declare PhotoCursor cursor for select nChild
from sxPhotoObj open PhotoCursor while (11)
begin fetch next from PhotoCursor into
_at_count if (_at__at_fetch_status -1) break set
_at_sum _at_sum _at_count end close
PhotoCursor deallocate PhotoCursor print 'Sum
is 'cast(_at_sum as varchar(12))
select count() from sxPhotoObj where nChild
gt 0
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Performance (on current SDSS data)
  • Run times on 15k HP Server (2 cpu, 1 GB , 8
    disk)
  • Some take 10 minutes
  • Some take 1 minute
  • Median 22 sec.
  • Ghz processors are fast!
  • (10 mips/IO, 200 ins/byte)
  • 2.5 m rec/s/cpu

1,000 IO/cpu sec 64 MB IO/cpu sec
65
Then What?
  • 20 Queries were a way to engage
  • Needed spatial data library
  • Needed DB design
  • Built website to publish the data
  • Data Loading (workflow scheduler).
  • Pixel web service that evolved
  • SkyQuery federation evolved
  • Batch job system MyDB
  • Multi-Archive Queries (Cross Match)
  • Now focused on spatial data library.
    Conversion to put analysis in DB

66
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.

67
Call To Action
  • This is the ground floor of eScience.
  • If you are a computer scientist, pair
    with a X-Science and dive in.
  • If you are a X-Scientist (X ? Computer)
    find a CS buddy.
  • This buddy system is mostly chemistry Not
    everyone is a collaborative. Not everyone is
    cross-disciplinary. There are risks (shared fate
    error 33).

68
eScience  The Next Decade Will Be Exciting.
  • All scientific data and literature is coming
    online and will be cross-indexed.
  • Funding agencies are forcing the scientific
    literature into the public domain. Scientific
    data, traditionally horded by investigators (with
    notable exceptions), will also become public. 
  • The forced electronic publication of scientific
    literature and data poses some deep technical
    questions just exactly how does anyone read and
    understand it now and a century from now?
  • Each intellectual discipline X is building an
    X-informatics and computational-X
    branch. Progress has been astonishing, but the
    real changes will happen in the next decade.
  • The X-info branches, in collaboration with
    computer science, must cooperate to solve these
    problems. Ive been pursuing these questions in
    Geography (with http//TerraService.Net),
    Astronomy (with the World-Wide telescope -- e.g.
    http//SkyServer.Sdss.org and http//www.ivoa.net/
    ) and more recently in bio informatics (with
    portable PubMedCentral).

69
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

70
Outline
  • The Evolution of X-Info
  • Online Literature
  • Online Data
  • The World Wide Telescope as Archetype

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

71
References http//SkyServer.SDSS.org/http//rese
arch.microsoft.com/pubs/ http//research.microsof
t.com/Gray/SDSS/ (download personal SkyServer)
  • Data Mining the SDSS SkyServer DatabaseJim 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
    Surveys (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 SkyServerPublic Access to Sloan Digital Sky
    Server DataJim 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 TelescopeJim 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
    ThakarJune 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, 
  • with Alexander S. Szalay, Gyorgy Fekete,  Wil
    OMullane, Aniruddha R. Thakar, Gerd Heber, 
    Arnold H. Rots, MSR-TR-2004-32,
  • Extending the SDSS Batch Query System to the
    National Virtual Observatory Grid, 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.

72
Schema (aka metadata)
  • Everyone starts with the same schema
    ltstuff/gtThen the start arguing about semantics.
  • Virtual Observatory http//www.ivoa.net/
  • Metadata based on Dublin Corehttp//www.ivoa.net
    /Documents/latest/RM.html
  • Universal Content Descriptors (UCD)
    http//vizier.u-strasbg.fr/doc/UCD.htxCaptures
    quantitative concepts and their unitsReduced
    from 100,000 tables in literature to 1,000
    terms
  • VOtable a schema for answers to
    questionshttp//www.us-vo.org/VOTable/
  • Common QueriesCone Search and Simple Image
    Access Protocol, SQL
  • Registry http//www.ivoa.net/Documents/latest/RME
    xp.htmlstill a work in progress.

73
References http//SkyServer.SDSS.org/http//rese
arch.microsoft.com/pubs/ http//research.microsof
t.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,
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