1. 1 Four Talks The article we actually wrote Online Scientific Publication, Curation, Archiving The advertised talk Computer Science Challenges in the VO A Web Server (SkyServer) tour A Web Service (SdssCutout) tour These lead up to Alex Szalay’s talk on Web Services Jim Gray, Microsoft Alex Szalay, Ani Thakar, Jan Vandenberg, JHU Chris Stoughton, Fermilab

2. 2 Online Scientific Publication, Curation, Archiving The Paper We Wrote Online Scientific Publication, Curation, Archiving Jim Gray, Microsoft Alex Szalay, Ani Thakar, Jan Vandenberg, JHU Chris Stoughton, Fermilab

3. 3 Outline Virtual Observatory will be an ecosystem of authors, curators, publishers, archivers, readers contributing & using shared data. The process and roles author, curator, publisher, archiver roles are changing Ephemeral & derived Must capture ephemeral information. All design & metadata info is ephemeral Can tradeoff recomputing derived data Economics Publish/Archive cost is zero, Author/Curator cost dominates SDSS data inflation what we are doing

4. 4 Premise Once published, scientific data needs to be available forever, so that the science can be reproduced/extended. What does that mean? –Data Ephemeral Data: could not be reproduced Stable data: could be drived from emphemeral data. –Meta-data: how the data was collected/derived is ephemeral Must be preserved Includes design docs, software, email, pubs, personal notes

5. 5 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 centralized 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

6. 6 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

7. 7 The Core Problem: No Economic Model The archive user has not yet been 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 very hard and there is no reward for doing it. The results are rewarded, not the process of getting them. Storage/archive NOT the problem (it’s almost free) Curating/Publishing is expensive.

8. 8 What SDSS is Doing: Capture the Bits Best-effort documenting data and process. Publishing data: often by UPS (~ 5TB today (dr1) and so ~15k$ 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.

9. 9 SDSS 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

10. 10 Summary Virtual Observatory will be an ecosystem of authors, curators, publishers, archivers, readers contributing & using shared data. The process and roles are changing author + project = publisher + curator Ephemeral & stable data Capture ephemeral information. All design & metadata info is ephemeral Can tradeoff recomputing derived data Economics Author/Curate cost dominates SDSS Data Inflation, Data Pyramid

11. 11 Four Talks The article we actually wrote Online Scientific Publication, Curation, Archiving The advertised talk Computer Science Challenges in the VO A Web Server (SkyServer) tour A Web Service (SdssCutout) tour These lead up to Alex Szalay’s talk on Web Services

12. 12 Computer Science Challenges in the VO The Advertised Talk Computer Science Challenges in the VO Jim Gray, Microsoft

13. 13 Virtual Observatory Premise: Most data is (or could be online) So, 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 (no working at night, no clouds no moons no..). –It’s a smart telescope: links objects and data to literature on them.

14. 14 Virtual Observatory Data Federation of Web Services Massive datasets live near their owners: –Near the instrument’s software pipeline –Near the applications –Near data knowledge and curation –Computer centers become Data Centers Archives are replicated for –Performance –Availability/Reliability 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

15. 15 Some Unique Things About Astro Data There is a desire to compare data from different instruments –Most astronomers publish their data (especially surveys) –Combining data from different instruments gives more info –Szalay observes Metcalf’s law: utility grows as N 2 –This is less true in some other fields It’s tractable –sizes fit in current regimes (10s of terabytes today) –tasks fit Beowulfs Astro data is great sandbox for CS research. –High-dimensional data –Temporal, spatial, image datatypes –Few privacy/commercial concerns –There is lots of it

16. 16 My #1 Challenge: going beyond files (a file is an array of bytes) Science vs Commerce Data in files FTP a local copy /subset. ASCII or Binary. Each scientist builds own analysis toolkit Analysis is tcl script of toolkit on local data. Some simple visualization tools: x vs y Data in a database Standard reports for standard things. Report writers for non-standard things GUI tools to explore data. –Decision trees –Clustering –Anomaly finders

17. 17 But…some science is hitting a wall FTP and GREP are not adequate 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 ~10,000 disks At some point you need indices to limit search parallel data search and analysis search and analysis tools This is where databases can help You can FTP 1 MB in 1 sec You can FTP 1 GB / min (= 1 $/GB) … 2 days and 1K$ … 3 years and 1M$

18. 18 What’s needed? (not drawn to scale) Science Data & Questions Scientists Database To store data Execute Queries Plumbers Data Mining Algorithms Miners Question & Answer Visualization Tools

19. 19 CS Challenges For Astronomers Objectify your field: –Precisely define what you are talking about. –Objects and Methods / Attributes –This is REALLY difficult. –UCDs are a great start but, there is a long way to go “Software is like entropy, it always increases.” -- Norman Augustine, Augustine’s Laws –Beware of legacy software – cost can eat you alive –Share software where possible. –Use standard software where possible. –Expect it will cost you 25% to 40% of project.  Explain what you want to do with the VO –20 queries or something like that. Science Data & Questions Scientists

20. 20 Challenge to Data Miners: Linear and Sub-Linear Algorithms Today most correlation / clustering algorithms are polynomial N 2 or N 3 or… N 2 is VERY big when N is big (10 18 is big) Need sub-linear algorithms Current approaches are near optimal given current assumptions. So, need new assumptions probably heuristic and approximate Data Mining Algorithm s Miners Techniques

21. 21 Challenge to Data Miners: Rediscover Astronomy Astronomy needs deep understanding of physics. But, some was discovered as variable correlations then “explained” with physics. Famous example: Hertzsprung-Russell Diagram star luminosity vs color (=temperature) Challenge 1 (the student test): How much of astronomy can data mining discover? Challenge 2 (the Turing test): Can data mining discover NEW correlations? Data Mining Algorithm s Miners

22. 22 Plumbers: Organize and Search Petabytes Automate –instrument-to-archive pipelines It is is a messy business – very labor intensive Most current designs do not scale (too many manual steps) BaBar (1TB/day) and ESO pipeline seem promising. A job-scheduling or workflow system –Physical Database design & access Data access patterns are difficult to anticipate Aggressively and automatically use indexing, sub-setting. Search in parallel Goals –Answer easy queries in 10 seconds. –Answer hard queries (correlations) in 10 minutes. Database To store data Execute Queries Plumbers

23. 23 Q: How can a computer scientist help, without learning a LOT of Astronomy? A: Scenario Design: 20 questions. Astronomers proposed 20 questions Typical of things they want to do Each would require a week (or month) of programming in tcl / C++/ FTP Goal, make it easy to answer questions DB and tools design motivated by this goal –Implemented DB & utility procedures –JHU Built GUI for Linux 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) 15M Photo Objects ~ 400 attributes 50K Spectra with ~30 lines/ spectrum

26. 26 An Easy Query Q15: Find asteroids Sounds hard but there are 5 pictures of the object at 5 different times (color filters) and so can “see” velocity. Image pipeline computes velocity. Computing it from the 5 color x,y would also be fast Finds 1,303 objects in 3 minutes, 140MBps. (could go 2x faster with more disks) selectobjId, dbo.fGetUrlEq(ra,dec) as url --return object ID & url sqrt(power(rowv,2)+power(colv,2)) as velocity fromphotoObj -- check each object. where (power(rowv,2) + power(colv, 2)) -- square of velocity between 50 and 1000 -- huge values =error

27. 27 Q15: Fast Moving Objects Find near earth asteroids: Finds 3 objects in 11 minutes –(or 52 seconds with an index) Ugly, but consider the alternatives (c programs an files and…) – SELECT r.objID as rId, g.objId as gId, dbo.fGetUrlEq(g.ra, g.dec) as url FROM PhotoObj r, PhotoObj g WHERE r.run = g.run and r.camcol=g.camcol and abs(g.field-r.field)<2 -- nearby -- the red selection criteria and ((power(r.q_r,2) + power(r.u_r,2)) > 0.111111 ) and r.fiberMag_r between 6 and 22 and r.fiberMag_r < r.fiberMag_g and r.fiberMag_r < r.fiberMag_i and r.parentID=0 and r.fiberMag_r < r.fiberMag_u and r.fiberMag_r < r.fiberMag_z and r.isoA_r/r.isoB_r > 1.5 and r.isoA_r>2.0 -- the green selection criteria and ((power(g.q_g,2) + power(g.u_g,2)) > 0.111111 ) and g.fiberMag_g between 6 and 22 and g.fiberMag_g < g.fiberMag_r and g.fiberMag_g < g.fiberMag_i and g.fiberMag_g < g.fiberMag_u and g.fiberMag_g < g.fiberMag_z and g.parentID=0 and g.isoA_g/g.isoB_g > 1.5 and g.isoA_g > 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())< 4.0 and abs(r.fiberMag_r-g.fiberMag_g)< 2.0

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29. 29 Data Visualization (and human-computer interface) Make it easy to ask questions Make it easy to understand the answers. Bad news: we have had no takers on the “visualization 20 questions” This is still a VERY retro area. But. The following demos show some progress. Question & Answer Visualization ToolsTools

30. 30 Four Talks The article we actually wrote Online Scientific Publication, Curation, Archiving The advertised talk Computer Science Challenges in the VO A Web Server (SkyServer) tour A Web Service (SdssCutout) tour These lead up to Alex Szalay’s talk on Web Services

31. 31 SkyServer Tour http://skyserver.sdss.org/ http://skyserver.sdss.org/ Shows benefit of a database –everything online –Easy to find things – index helps –Automatic parallel search is essential Beware: –I’m a lunatic re using databases for everything –Most people do not put images in DB –I do, because it is Simpler Easier to manage The right thing to do.

32. 32 Four Talks The article we actually wrote Online Scientific Publication, Curation, Archiving The advertised talk Computer Science Challenges in the VO A Web Server (SkyServer) tour A Web Service (SdssCutout) tour Leads up to Alex Szalay’s talk on Web Services

33. 33 What’s a Web Service 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 Your program Data In your address space Web Service soap object in xml You Web Server http url Web page

34. 34 Federation Data Federations of Web Services Massive datasets live near their owners: –Near the instrument’s software pipeline –Near the applications –Near data knowledge and curation –Super Computer centers become Super Data Centers Each Archive publishes web services –Schema: documents the data –Methods on objects (queries) Scientists get “personalized” extracts Uniform access to multiple Archives –A common global schema

35. 35 Grid and Web Services Synergy I believe the Grid will be many web services IETF standards Provide –Naming –Authorization / Security / Privacy –Distributed Objects Discovery, Definition, Invocation, Object Model –Higher level services: workflow, transactions, DB,.. Synergy: commercial Internet & Grid tools

36. 36 SDSS Cutout http://SkyService.pha.jhu.edu/SdssCutout/ http://SkyService.pha.jhu.edu/SdssCutout/ A simple web service You can have a copy of the code Needs an online database backend

37. 37 Four Talks The article we actually wrote Online Scientific Publication, Curation, Archiving The advertised talk Computer Science Challenges in the VO A Web Server (SkyServer) tour A Web Service (SdssCutout) tour Leads up to Alex Szalay’s talk on Web Services

38. 38 References and Links SkyServer –http://skyserver.sdss.org/http://skyserver.sdss.org/ –http://SkyService.pha.jhu.edu/SdssCutout/http://SkyService.pha.jhu.edu/SdssCutout/ Virtual Observatory –http://www.us-vo.org/http://www.us-vo.org/ –http://www.voforum.org/http://www.voforum.org/ World-Wide Telescope –paper in Science V.293 pp. 2037-2038. 14 Sept 2001. (MS-TR-2001-77 word or pdf.)wordpdf.) SDSS DB: –Get your personal copy at http://research.microsoft.com/~gray/sdss http://research.microsoft.com/~gray/sdss