1. 1 Online Science the New Computational Science Jim Gray Microsoft Research http://research.microsoft.com/~gray Alex Szalay Johns Hopkins

2. 2 Outline The Evolution of X-Info – how CS can help The World Wide Telescope as Archetype How I work with them: a case study 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 The Big Problems Experiments & Instruments Simulations facts answers questions Literature Other Archives facts ?

3. 3 Evolving Science Empirical Science –Scientist gathers data by direct observation –Scientist analyzes data Analytical Science –Scientist builds analytical model –Makes predictions. 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

4. 4 Information Avalanche In science, industry, government,…. –better observational instruments and –and, better simulations producing a data avalanche 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 New emphasis on informatics: –Capturing, Organizing, Summarizing, Analyzing, Visualizing Image courtesy C. Meneveau & A. Szalay @ JHU BaBar, Stanford Space Telescope P&E Gene Sequencer From http://www.genome.uci.edu/

5. 5 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..

6. 6 Why Is Astronomy Special? Almost all literature online and public ADS: http://adswww.harvard.edu/ CDS: http://cdsweb.u-strasbg.fr/ http://adswww.harvard.edu/ http://cdsweb.u-strasbg.fr/ Data has no commercial value – No privacy concerns, freely share results with others – Great for experimenting with algorithms It is real and well documented – High-dimensional (with confidence intervals) – Spatial, temporal Diverse and distributed – Many different instruments from many different places and many different times The community wants to share the data There is a lot of it (soon petabytes) IRAS 100  ROSAT ~keV DSS Optical 2MASS 2  IRAS 25  NVSS 20cm WENSS 92cm GB 6cm

7. 7 Time and Spectral Dimensions The 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. Slide courtesy of Robert Brunner @ CalTech. Crab star 1053 AD

8. 8 Federation 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

9. 9 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$

10. 10 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 …

11. 11 What’s X-info Needs from us (cs) (not drawn to scale) Science Data & Questions Scientists Database To store data Execute Queries Plumbers Data Mining Algorithms Miners Question & Answer Visualization Tools

12. 12 Your program Data In your address space Web Service soap object in xml Your program Web Server http Web page 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 models for each science.

13. 13 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 N 2, likelihood techniques N 3 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

14. 14 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

15. 15 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.

16. 16 SkyQuery ( http://skyquery.net/) 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

17. 17 2MASS INT SDSS FIRST SkyQuery Portal Image Cutout SkyQuery Structure Each SkyNode publishes –Schema Web Service –Database Web Service Portal is –Plans Query (2 phase) –Integrates answers –Is itself a web service

18. 18 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

19. 19 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)

20. 20 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.

21. 21 Outline The Evolution of X-Info – how CS can help The World Wide Telescope as Archetype How I work with them: a case study 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 The Big Problems Experiments & Instruments Simulations facts answers questions Literature Other Archives facts ?

22. 22 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

23. 23 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

24. 24 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 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 (r ¼ 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

25. 25 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

26. 26 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. Selectcast((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 bycast((u-g) as int), cast((g-r) as int), cast((r-i) as int), cast((i-z) as int) order by count(*)

27. 27 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).

28. 28 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.

29. 29 Outline The Evolution of X-Info – how CS can help The World Wide Telescope as Archetype How I work with them: a case study 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 The Big Problems Experiments & Instruments Simulations facts answers questions Literature Other Archives facts ?

30. 30 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

31. 31 http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs http://research.microsoft.com/Gray http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs http://research.microsoft.com/Gray References http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs / http://research.microsoft.com/Gray/SDSS/ (download personal SkyServer) http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs / http://research.microsoft.com/Gray/SDSS/ 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. WordPDF The World-Wide Telescope Jim Gray; Alexander Szalay August 2001 6 p.: Word 684 Kbytes PDF 84 KbytesWordPDF 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)WordPDF 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. http://www.sdss.org/ There Goes the Neighborhood: Relational Algebra for Spatial Data Search, There Goes the Neighborhood: Relational Algebra for Spatial Data Search with Alexander S. Szalay, Gyorgy Fekete, Wil O’Mullane, 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.Extending the SDSS Batch Query System to the National Virtual Observatory Grid,

32. 32 Outline New Science Working cross disciplines Data Demographics and Data Handling –Exponential growth –Data Lifecycle –Versions –Data inflation –Year 5 –Overprovision by 6x –Data Loading –Regression Tests –Statistical subset Curation ? Experiments & Instruments Simulations facts answers questions Literature Other Archives facts

33. 33 Information Avalanche In science, industry, government,…. –better observational instruments and –and, better simulations producing a data avalanche 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 New emphasis on informatics: –Capturing, Organizing, Summarizing, Analyzing, Visualizing Image courtesy C. Meneveau & A. Szalay @ JHU BaBar, Stanford Space Telescope P&E Gene Sequencer From http://www.genome.uci.edu/

34. 34 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

35. 35 CERN Tier 0 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 Data pyramid: 100GB : 1TB : 100TB : 1PB : 10PB

36. 36 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 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

37. 37 Moore’s Law in Proteomics Roche Center for Medical Genomics (RCMG): number of mass-spectra acquired for proteomics doubled every year since first mass spectrometer deployed.mass-spectra R 2 =0.96 Courtesy of Peter Berndt, Roche Center for Medical Genomics (RCMG)

38. 38 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. instrument or simulator pipeline other data other data pipeline Level 0 raw Level 1 calibrated Level 2 derived

39. 39 Data Inflation – Data Pyramid Level 1A Grows X TB/year ~.4X TB/y compressed (level 1A in NASA terms) Level 2 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 ~ N 2 bytes Net: Data Inflation: L2 ≥ L1 E1 E2 E3 E4 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. time Level 1A4 editions of 4 Level 2 products

40. 40 The Year 5 Problem Data arrives at R bytes/year New Storage & Processing –Need to buy R units in year N Data inflation means ~N 2 R –Need to buy NR units Depreciate over 3 years –After year 3 need to buy N 2 R + (N-3) 2 R Moore’s law: 60%/year price decline Capital expense peaks at year 5 See 6x Over-Power slide next

41. 41 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 DR3C 2.4TB DR2C 1.8TB DR2M 1.8TB DR2P 1.8TB DR3M 2.4TB DR3P 2.4TB

42. 42 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!

43. 43 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.

44. 44 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.

45. 45 Outline New Science Working cross disciplines Data Demographics and Data Handling Curation –Problem statement –Economics –Astro as a case in point ? Experiments & Instruments Simulations facts answers questions Literature Other Archives facts

46. 46 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/derived is primary Must be preserved Includes design docs, software, email, pubs, personal notes, teleconferences, NASA “level 0”

47. 47 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)

48. 48 Publishing Data Roles Authors Publishers Curators Archives Consumers Traditional Scientists Journals Libraries Archives Scientists Emerging Collaborations Project web site Data+Doc Archives Digital Archives Scientists

49. 49 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

50. 50 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 organize/search all this metadata.

51. 51 Schema (aka metadata) Everyone starts with the same schema Then the start arguing about semantics. Virtual Observatory: http://www.ivoa.net/ http://www.ivoa.net/ Metadata based on Dublin Core: http://www.ivoa.net/Documents/latest/RM.html 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 http://vizier.u-strasbg.fr/doc/UCD.htx VOtable – a schema for answers to questions http://www.us-vo.org/VOTable/ 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. http://www.ivoa.net/Documents/latest/RMExp.html

52. 52 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

53. 53 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

54. 54 http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs http://research.microsoft.com/Gray http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs http://research.microsoft.com/Gray References http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs / http://research.microsoft.com/Gray/SDSS/ (download personal SkyServer) http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs / http://research.microsoft.com/Gray/SDSS/ Extending the SDSS Batch Query System to the National Virtual Observatory Grid, 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, 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, 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 Petabyte Scale Data Mining: Dream or Reality?, Online Scientific Data Curation, Publication, and Archiving, 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 ScienceThe 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 DataThe 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 TelescopeThe 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 SurveyDesigning & 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,