1. The World Wide Telescope – a Digital Library Prototype Jim Gray, Microsoft Research Alex Szalay, Johns Hopkins University Talk at OCLC @ Dublin, OH, 17 May 2004 http://research.microsoft.com/~gray/talks/OCLC_WWT.ppt

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

3. 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

4. Whats Happening? We are drowning in information Single fixed hierarchy is hopeless –Cant organize/find things in a simple tree HOPE: schematized storage –Objects have Dublin-like facets –Most facets acquired automatically (email, photo, doc,…) –Users add annotations and relationships Librarians call this accession Automate accession as much as possible Folders/directories are standing queries –Organization is search based demo sis. Interesting (public) research projects –Stuff Ive Seen: http://research.microsoft.com/adapt/sis/ http://research.microsoft.com/adapt/sis/ –MyLifebits: http://research.microsoft.com/barc/mediapresence/MyLifeBits.aspx http://research.microsoft.com/barc/mediapresence/MyLifeBits.aspx Longhorn product embraces & extends these ideas.

5. The World Wide Telescope – a Digital Library Prototype Jim Gray, Microsoft Research Alex Szalay, Johns Hopkins University Talk at OCLC @ Dublin, OH, 17 May 2004 http://research.microsoft.com/~gray/talks/OCLC_WWT.ppt But, what about the talk I promised you?

6. The Talk Libraries morphing to integrated text + data (you know that) Dublin Core is bedrock, but many issues remain. (you know that) WWT: All Astronomy data and literature online and integrated Problems Librarians have grappled with for centuries: curation, preservation, indexing, access, summarization. 1.Overview of the World-Wide Telescope as a digital library 2.Focus on metadata, schema, curation, and preservation.. Candidly, we have more problems than solutions, but the data is arriving and we are doing the best we can.

7. 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) synthesizing theory, experiment and computation with advanced data management and statistics

8. The Big Picture Experiments & Instruments Simulations facts answers questions 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 Literature Other Archives facts

9. The Virtual Observatory Premise: most data is (or could be online) 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 –Its a smart telescope: links objects and data to literature Software is the capital expense –Share, standardize, reuse..

10. 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

11. 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

12. 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 Data will reside with projects –Analyses must be close to the data Roles Authors Publishers Curators Consumers Traditional Scientists Journals Libraries Scientists Emerging Collaborations Project www site Bigger Archives Scientists

13. 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

14. The Core Problem: No Economic Model The archive user has not yet been born. How can he pay you to curate the data? Q: The Scientist gathered data for his own purpose. Why should he pay (invest time) for your needs? A: thats the scientific method Curating data (documenting the design, the acquisition, and the processing) is very hard and there is no reward for doing it. Results are rewarded, not the process of getting them. Storage/archive NOT the problem (its almost free) Curating/Publishing is expensive. Better standards & tools lower costs

15. Data Inflation – Data Pyramid Level 1A Grows 5TB pixels/year growing to 25TB ~ 2 TB/y compressed growing to 13TB ~ 4 TB today (level 1A in NASA terms) Level 2 Derived data products ~10x smaller But there are many catalogs. 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 Level 2 products

16. What SDSS is Doing: Capture the Bits Best-effort documenting data and process. 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. Now we need to figure out how to organize/search all this metadata.

17. Making Discoveries Where are discoveries made? –At the edges and boundaries –Going deeper, collecting more data, using more colors…. Metcalfes 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

18. Federation Global Federations 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

19. 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

20. Data Access is Hitting a Wall 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 ~4,000 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 $/GB) You can FTP 1 TB in 2 days and 1K$ You can FTP 1 PB in 3 years and 1M$ Current practice of data download (FTP/GREP) will not scale to petabyte datasets

21. Smart Data Better Data Schemas 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 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.

22. 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 Moores Law, we can only keep up with N logN A way out? –Relax optimal notion (data is fuzzy, answers are approximate) –Dont assume infinite computational resources or memory Requires combination of statistics & computer science

23. 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

24. 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/

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

26. 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

27. 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

28. 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

29. National Center Biotechnology Information (NCBI) A Better 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

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

31. The Talk Libraries morphing to integrated text + data (you know that) Dublin Core is bedrock, but many issues remain. (you know that) WWT: All Astronomy data and literature online and integrated Problems Librarians have grappled with for centuries: curation, preservation, indexing, access, summarization. 1.Overview of the World-Wide Telescope as a digital library 2.Focus on metadata, schema, curation, and preservation.. Candidly, we have more problems than solutions, but the data is arriving and we are doing the best we can.

32. Education Educational Projects, aimed at advanced high school students, but covering middle school Teach how to analyze data, discover patterns, not just astronomy 3.7 million project hits, 1.25 million page views of educational content More than 4000 textbooks On the whole web site: 44 million web hits Largely a volunteer effort by many individuals Matches the 2020 curriculum