1. 1 Where The Rubber Meets the Sky Giving Access to Science Data Jim Gray Microsoft Research Gray@Microsoft.com Http://research.Microsoft.com/~Gray Alex Szalay Johns Hopkins University Szalay@JHU.edu Talk at U. Waterloo & U. Penn Fall 2004

2. 2 Promised Abstract: On-Line Science: The World-Wide Telescope as a Prototype for the New Computational Science. Computational science has historically meant simulation; but, there is an increasing role for analysis and mining of online scientific data. As a case in point, half of the world's astronomy data is public. The astronomy community is putting all that data on the Internet so that the Internet becomes the world's best telescope: it has the whole sky, in many bands, and in detail as good as the best 2-year-old telescopes. It is useable by all astronomers everywhere. This is the vision of the Virtual Observatory -- also called the World Wide Telescope (WWT). As one step along that path I have been working with the Sloan Digital Sky Survey and CalTech to federate their data in web services on the Internet, and to make it easy to ask questions of the database (see http://skyserver.sdss.org and http://skyquery.net). This talk explains the rationale for the WWT, discusses how we designed the database, and talks about some data mining tasks. It also describes computer science challenges of publishing, federating, and mining scientific data, and argues that XML web services are key to federating diverse data sources.http://skyserver.sdss.orghttp://skyquery.net Actual Abstract: I have been working with some astronomers for the last 6 years trying to apply DB technology to science problems. These are some lessons I learned.

3. 3 Outline New Science –X-Info for all fields X –WWT as an example –Big Picture –Puzzle –Hitting the wall –Needle in haystack –Mohamed and the mountain Working cross disciplines Data Demographics and Data Handling Curation ? Experiments & Instruments Simulations facts answers questions Literature Other Archives facts

4. 4 New 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 instruments Or generated by simulator –Processed by software –Scientist analyzes database / files

5. 5 The Big Picture Data ingest Managing a petabyte Common schema How to organize it? How to reorganize it? How to coexist with others? Data Query and Visualization tools Support/training Performance –Execute queries in a minute –Batch (big) query scheduling The Big Problems Experiments & Instruments Simulations facts answers questions ? Literature Other Archives facts

6. 6 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 …

7. 7 What 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

8. 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. 9 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/

10. 10 Next-Generation Data Analysis Looking for –Needles in haystacks – the Higgs particle –Haystacks: dark matter, dark energy, turbulence, ecosystem dynamics 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 Moore’s Law, we can only keep up with N logN A way out? –Relax optimal notion (data is fuzzy, answers are approximate) –Don’t assume infinite computational resources or memory Requires combination of statistics & computer science

11. 11 Smart Data: Unifying DB and Analysis There is too much data to move around Do data manipulations at database –Build custom procedures and functions into DB –Unify data Access & Analysis –Examples Statistical sampling and analysis Temporal and spatial indexing Pixel processing Automatic parallelism Auto (re)organize Scalable to Petabyte datasets Move Mohamed to the mountain, not the mountain to Mohamed.

12. 12 Outline New Science Working cross disciplines –How to help? –20 questions –WWT example –Alternative: CS Process Models Data Demographics and Data Handling Curation ? Experiments & Instruments Simulations facts answers questions Literature Other Archives facts

13. 13 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

14. 14 Working Cross-Culture A Way to Engage With Domain Scientists Communicate in terms of scenarios Work on a problem that gives 100x benefit –Weeks/task vs hours/task Solve 20% of the problem –The other 80% will take decades Prototype Go from working-to-working, Always have –Something to show –Clear next steps –Clear goal Avoid death-by-collaboration-meetings.

15. 15 Working Cross-Culture -- 20 Questions: A Way to Engage With Domain Scientists Astronomers proposed 20 questions Typical of things they want to do Each would require a week or more in old way (programming in tcl / C++/ FTP) Goal, make it easy to answer questions This goal motivates DB and tools design

16. 16 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..

17. 17 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 World Wide Telescope: federate all 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

18. 18 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

19. 19 Two kinds of SDSS data in an SQL DB (objects and images all in DB) 300M Photo Objects ~ 400 attributes 10B rows overall 400K Spectra with ~30 lines/ Spectrum 100 M rows

20. 20 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(*)

21. 21 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 evolved to 2003. SkyQuery federation evolved to 2004. Now focused on spatial data library. Conversion to OR DB (put analysis in DB).

22. 22 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.

23. 23 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

24. 24 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/

25. 25 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

26. 26 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

27. 27 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

28. 28 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)

29. 29 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

30. 30 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

31. 31 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

32. 32 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

33. 33 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!

34. 34 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.

35. 35 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.

36. 36 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

37. 37 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”

38. 38 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)

39. 39 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

40. 40 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

41. 41 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

42. 42 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.

43. 43 The OGIS model Ingest Archive Data Management Administer Access Producer Consumer

44. 44 Ingest Challenges Push vs Pull What are the gold standards? Automatic indexing, annotation, provenance. Auto-Migration (Format conversion) Version management How capture time varying sources Capture “dark matter” (encapsulated data) –Bits don’t “rust” but applications do.

45. 45 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 are different from the storage formats. migration? emulation? Gold Standards?)

46. 46 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)

47. 47 The Midrange Paradox Large archives are curated –Curated by projects Small archives are appendices to papers –Curated by journals Medium-sized archives are in limbo –No place to register them –No one has mandate to preserve them Example: –Your website with your data files –Small scale science projects –Genbank gets the sequence but not the software or analysis that produced it.

48. 48 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

49. 49 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

50. 50 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,

51. 51 How to Publish Data: Web Services Web SERVER: –Given a url + parameters –Returns a web page (often dynamic) Web SERVICE: –Given a XML document (soap msg) –Returns an XML document (with schema) –Tools make this look like an RPC. F(x,y,z) returns (u, v, w) –Distributed objects for the web. –+ naming, discovery, security,.. Internet-scale distributed computing Your program Data In your address space Web Service soap object in xml Your program Web Server http Web page

52. 52 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

53. 53 The Sloan Digital Sky Survey Goal –Create the most detailed map of the Northern Sky to-date 2.5m telescope –3 degree field of view Two surveys in one –5-color images of ¼ of the sky –Spectroscopic survey of a million galaxies and quasars Very high data volume –40 Terabytes of raw data –10 Terabytes processed –All data public The University of Chicago Princeton University The Johns Hopkins University The University of Washington New Mexico State University University of Pittsburgh Fermi National Accelerator Laboratory US Naval Observatory The Japanese Participation Group The Institute for Advanced Study Max Planck Inst, Heidelberg Sloan Foundation, NSF, DOE, NASA The University of Chicago Princeton University The Johns Hopkins University The University of Washington New Mexico State University University of Pittsburgh Fermi National Accelerator Laboratory US Naval Observatory The Japanese Participation Group The Institute for Advanced Study Max Planck Inst, Heidelberg Sloan Foundation, NSF, DOE, NASA

54. 54 SkyServer A multi-terabyte database An educational website –More than 50 hours of educational exercises –Background on astronomy –Tutorials and documentation –Searchable web pages Easy astronomer access to SDSS data. Prototype eScience lab Interactive visual tools for data exploration http://skyserver.sdss.org/

55. 55 Demo SkyServer atlas education project Mouse in pixel space Explore an object (record space) Explore literature Explore a set Pose a new question

56. 56 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 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

57. 57 2MASS INT SDSS FIRST SkyQuery Portal Image Cutout Demo SkyQuery Structure Each SkyNode publishes –Schema Web Service –Database Web Service Portal is –Plans Query (2 phase) –Integrates answers –Is itself a web service

58. 58 MyDB: eScience Workbench Prototype of bringing analysis to the data Everybody gets a workspace (database) –Executes analysis at the data –Store intermediate results there –Long queries run in batch –Results shared within groups Only fetch the final results Extremely successful – matches work patterns

59. 59 National Center Biotechnology Information (NCBI) A good Example PubMed: –Abstracts and books and.. GenBank: –All Gene sequences deposited –BLAST and other searches –Website to explore data and literature Entrez: –unifies many databases with literature (books, journals,..) –Organizes the data

60. 60 Publishing Data Exponential growth: –Projects last at least 3-5 years –Data sent upwards only at the end of the project –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 Roles Authors Publishers Curators Consumers Traditional Scientists Journals Libraries Scientists Emerging Collaborations Project www site Bigger Archives Scientists

61. 61 Making Discoveries Where are discoveries made? –At the edges and boundaries –Going deeper, collecting more data, using more colors…. Metcalfe’s law: quadratic benefit –Utility of computer networks grows as the number of possible connections: O(N 2 ) Data Federation: quadratic benefit –Federation of N archives has utility O(N 2 ) –Possibilities for new discoveries grow as O(N 2 ) Current sky surveys have proven this –Very early discoveries from SDSS, 2MASS, DPOSS

62. 62 The OGIS model Ingest Archive Data Management Administer Access Producer Consumer

63. 63 Jim’s Model of Library Science Alexandria Gutenberg (Melvil) Dewey Decimal MARC (Henriette Avram) Dublin Core Yes, I know there have been other things.

64. 64 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 –HasPart –IsReferencedBy –References –IsFormatOf –HasFormat –ConformsTo –Spatial –Temporal –Mediator –DateAccepted –DateCopyrighted –DateSubmitted –EducationalLevel –AccessRights –BibliographicCitation 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) Types –Collection –Dataset –Event –Image –InteractiveResouce –Service –Software –Sound –Text –PhysicalObject –StillImage –MovingImage

65. 65 Ingest Challenges Push vs Pull What are the gold standards? Automatic indexing, annotation, provenance. Auto-Migration (Format conversion) Version management How capture time varying sources Capture “dark matter” (encapsulated data) –Bits don’t “rust” but applications do.

66. 66 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 are different from the storage formats. migration? emulation? Gold Standards?)

67. 67 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)

68. 68 The Midrange Paradox Large archives are curated –Curated by projects Small archives are appendices to papers –Curated by journals Medium-sized archives are in limbo –No place to register them –No one has mandate to preserve them Example: –Your website with your data files –Small scale science projects –Genbank gets the sequence but not the software or analysis that produced it.