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Online Science the New Computational Science Jim Gray Microsoft Research http://research.microsoft.com/~gray Alex Szalay Johns Hopkins 1 Outline • The Evolution of X-Info – how CS can help • The World Wide Telescope as Archetype • How I work with them: a case study Experiments & Instruments Other Archives facts Literature facts questions ? answers Simulations The Big Problems • • • • • • Data ingest Managing a petabyte Common schema How to organize it How to reorganize it How to coexist with others • • • • Query and Vis tools Integrating data and Literature Support/training Performance – Execute queries in a minute – Batch query scheduling 2 Evolving Science • Empirical Science – Scientist gathers data by direct observation – Scientist analyzes data • Analytical Science – Scientist builds analytical model – Makes predictions. 2 . 4G c2 a a 3 a 2 • 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 3 Information Avalanche • In science, industry, government,…. – better observational instruments and – and, better simulations producing a data avalanche Image courtesy C. Meneveau & A. Szalay @ JHU • 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 BaBar, Stanford P&E Gene Sequencer From http://www.genome.uci.edu/ • New emphasis on informatics: – Capturing, Organizing, Summarizing, Analyzing, Visualizing 4 Space Telescope 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.. 5 Why Is Astronomy Special? • Almost all literature online and public ADS: http://adswww.harvard.edu/ CDS: http://cdsweb.u-strasbg.fr/ • Data has no commercial value IRAS 25m 2MASS 2m – No privacy concerns, freely share results with others DSS Optica – Great for experimenting with algorithms • It is real and well documented – High-dimensional – Spatial, temporal (with confidence intervals) IRAS 100m • Diverse and distributed – Many different instruments from many different places and many different times WENSS 92cm NVSS 20cm • The community wants to share the data • There is a lot of it (soon petabytes) 6 ROSAT ~keV GB 6cm Time and Spectral Dimensions The Multiwavelength Crab Nebulae Crab star 1053 AD X-ray, optical, infrared, and radio views of the nearby Crab Nebula, which is now in a state of chaotic expansion after a supernova explosion first sighted in 1054 A.D. by Chinese Astronomers. 7 Slide courtesy of Robert Brunner @ CalTech. 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 Federation 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. • • • • You can FTP 1 MB in 1 sec You can FTP 1 GB / min (~1$) … 2 days and 1K$ … 3 years and 1M$ • 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 9 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 10 • … What’s X-info Needs from us (cs) (not drawn to scale) Miners Scientists Science Data & Questions Data Mining Algorithms Plumbers Database To store data Execute Queries Question & Answer Visualization Tools 11 Web Services: Enable Federation • Web SERVER: – Given a url + parameters – Returns a web page (often dynamic) Your program Web Server • 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. Your program Data In your address space Web Service 12 New Approaches to Data Analysis • Looking for – Needles in haystacks – the Higgs particle – Haystacks: Dark matter, Dark energy • Needles are easier than haystacks • Global statistics have poor scaling – Correlation functions are N2, likelihood techniques N3 • As data and computers grow at same rate, we can only keep up with N logN • A way out? – Discard notion of optimal (data is fuzzy, answers are approximate) – Don’t assume infinite computational resources or memory • Requires combination of statistics & computer science From Alex Szalay 13 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 14 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. 15 SkyQuery (http://skyquery.net/) • Distributed Query tool using a set of web services • Many astronomy archives from Pasadena, Chicago, Baltimore, Cambridge (England) • Has grown from 4 to 15 archives, now becoming international standard • WebService Poster Child • Allows queries like: SELECT o.objId, o.r, o.type, t.objId FROM 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 16 SkyQuery Structure • Each SkyNode publishes – Schema Web Service – Database Web Service • Portal is – Plans Query (2 phase) – Integrates answers – Is itself a web service Image Cutout SDSS INT SkyQuery Portal FIRST 2MASS 17 Outline • The Evolution of X-Info – how CS can help • The World Wide Telescope as Archetype • How I work with them: a case study Experiments & Instruments Other Archives facts Literature facts questions ? answers Simulations The Big Problems • • • • • • Data ingest Managing a petabyte Common schema How to organize it How to reorganize it How to coexist with others • • • • Query and Vis tools Integrating data and Literature Support/training Performance – Execute queries in a minute – Batch query scheduling 21 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 22 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 23 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 Also some good queries at: count of galaxies within 30"of it that have a photoz24 within http://www.sdss.jhu.edu/ScienceArchive/sxqt/sxQT/Example_Queries.html 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 (r¼ falloff of intensity on disk) and the photometric colors consistent with an elliptical galaxy. The deVaucouleours profile Q6: Find galaxies that are blended with a star, output the deblended galaxy magnitudes. Q7: Provide a list of star-like objects that are 1% rare. Q8: Find all objects with unclassified spectra. Q9: Find quasars with a line width >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.) 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 25 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. Select cast((u-g) as int) as ug, cast((g-r) as int) as gr, cast((r-i) as int) as ri, cast((i-z) as int) as iz, count(*) as Population from stars group by cast((u-g) as int), cast((g-r) as int), cast((r-i) as int), cast((i-z) as int) order by count(*) 26 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). 27 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. 28 Outline • The Evolution of X-Info – how CS can help • The World Wide Telescope as Archetype • How I work with them: a case study Experiments & Instruments Other Archives facts Literature facts questions ? answers Simulations The Big Problems • • • • • • Data ingest Managing a petabyte Common schema How to organize it How to reorganize it How to coexist with others • • • • Query and Vis tools Integrating data and Literature Support/training Performance – Execute queries in a minute – Batch query scheduling 29 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 30 References • • • http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs/ http://research.microsoft.com/Gray/SDSS/ (download personal SkyServer) 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. • The World-Wide Telescope • Jim Gray; Alexander Szalay August 2001 6 p.: Word 684 Kbytes PDF 84 Kbytes All astronomy data and literature will soon be online and accessible via the Internet. The community is building the Virtual Observatory, an organization of this worldwide data into a coherent whole that can be accessed by anyone, in any form, from anywhere. The resulting system will dramatically improve our ability to do multi-spectral and temporal studies that integrate data from multiple instruments. The virtual observatory data also provides a wonderful base for teaching astronomy, scientific discovery, and computational science. • Designing and Mining Multi-Terabyte Astronomy Archives • Robert J. Brunner; Jim Gray; Peter Kunszt; Donald Slutz; Alexander S. Szalay; Ani Thakar June 1999 8 p.: Word (448 Kybtes) PDF (391 Kbytes) The next-generation astronomy digital archives will cover most of the sky at fine resolution in many wavelengths, from X-rays, through ultraviolet, optical, and infrared. The archives will be stored at diverse geographical locations. One of the first of these projects, the Sloan Digital Sky Survey (SDSS) is creating a 5-wavelength catalog over 10,000 square degrees of the sky (see http://www.sdss.org/). The 200 million objects in the multi-terabyte database will have mostly numerical attributes in a 100+ dimensional space. Points in this space have highly correlated distributions. • There Goes the Neighborhood: Relational Algebra for Spatial Data Search, • Extending the SDSS Batch Query System to the National Virtual Observatory Grid, • with Alexander S. Szalay, Gyorgy Fekete, Wil O’Mullane, Aniruddha R. Thakar, Gerd Heber, Arnold H. Rots, MSR-TR-2004-32, 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. 31 Outline • New Science • Working cross disciplines • Data Demographics and Data Handling – Exponential growth – Data Lifecycle – Versions – Data inflation – Year 5 – Overprovision by 6x Experiments & Instruments – Data Loading – Regression Tests Other Archives facts facts – Statistical subset Literature • Curation ? Simulations questions answers 32 Information Avalanche • In science, industry, government,…. – better observational instruments and – and, better simulations producing a data avalanche Image courtesy C. Meneveau & A. Szalay @ JHU • 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 BaBar, Stanford P&E Gene Sequencer From http://www.genome.uci.edu/ • New emphasis on informatics: – Capturing, Organizing, Summarizing, Analyzing, Visualizing 33 Space Telescope 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 34 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 CERN Tier 0 Data pyramid: 100GB : 1TB : 100TB : 1PB : 10PB 35 • 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 1000 100 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 10 1 0.1 1970 1975 1980 1985 1990 1995 2000 CCDs Glass 36 Moore’s Law in Proteomics Courtesy of Peter Berndt, Roche Center for Medical Genomics (RCMG) Roche Center for Medical Genomics (RCMG): number of mass-spectra acquired for proteomics doubled every year since first mass spectrometer deployed. Count of Spectra Proteomics MS Data Generation 500000. R2=0.96 200000. 100000. 50000 20000 1998 1999 2000 2001 2002 Year 37 2003 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. Level 1 calibrated Level 0 raw instrument or simulator pipeline pipeline other data Level 2 derived other data 38 Data Inflation – Data Pyramid Level 2 Level 1A Grows X TB/year ~ .4X TB/y compressed (level 1A in NASA terms) 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 ~ N2 bytes • Net: Data Inflation: L2 ≥ L1 Level 1A 4 editions of 4 Level 2 products E4 E3 time E2 E1 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. 39 180 The Year 5 Problem Yearly Demand 160 Depreciated Inflated Demand • Data arrives at R bytes/year • New Storage & Processing – Need to buy R units in year N • Data inflation means Yearly Demand ( R ) 140 80 60 40 20 0 – Need to buy NR units 0 • Capital expense peaks at year 5 • See 6x Over-Power slide next 4 6 8 10 8 10 Yearly Capital Cost 4.0 3.5 Marginal Capital Cost 60%/year price decline 2 Year • Depreciate over 3 years • Moore’s law: Naive Demand 100 ~N2R – After year 3 need to buy N2R + (N-3)2R Inflated Demand 120 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0 2 4 6 Year 40 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 DR2C 1.8TB DR2M 1.8TB DR2P 1.8TB DR3C 2.4TB DR3M 2.4TB DR3P 2.4TB 41 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! LOAD Export EXP Check CSV CHK Build Task DBs BLD Build SQL Schema SQL Validate VAL Backup BCK Detach DTC PUBLISH Publish PUB Cleanup CLN Test Test Uniqueness Uniqueness Of Of Primary Primary Keys Keys FINISH FIN Test the unique Key in each table Test Test Foreign Foreign Keys Keys Test for consistency of keys that link tables Test Test Cardinalities Cardinalities Test consistency of numbers of various quantities Test Test HTM HTM IDs IDs Test Test parent-child parent-child consistency consistency Test the Hierarchical Triamgular Mesh IDs used for spatial indexing 42 Ensure that all parents and children and linked 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. 43 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. 44 Outline • • • • New Science Working cross disciplines Data Demographics and Data Handling Curation – Problem statement – Economics – Astro as a case in point Experiments & Instruments Other Archives facts Literature Simulations facts ? questions answers 45 Problem Statement • Once published, scientific data needs to be available forever, so that the science can be reproduced/extended. • What does that mean? NASA “level 0” – 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, 46 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) 47 Publishing Data Roles Traditional Emerging Authors Scientists Collaborations Publishers Journals Project web site Curators Libraries Data+Doc Archives Archives Archives Digital Archives Consumers Scientists Scientists 48 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 49 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 50 organize/search all this metadata. Schema (aka metadata) • Everyone starts with the same schema <stuff/> Then the start arguing about semantics. • Virtual Observatory: http://www.ivoa.net/ • Metadata based on Dublin Core: 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 • VOtable – a schema for answers to questions 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. 51 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 52 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 53 References http://SkyServer.SDSS.org/ http://research.microsoft.com/pubs/ http://research.microsoft.com/Gray/SDSS/ (download personal SkyServer) Extending the SDSS Batch Query System to the National Virtual Observatory Grid, M. A. Nieto-Santisteban, W. O'Mullane, J. Gray, N. Li, T. Budavari, A. S. Szalay, A. R. Thakar, MSR-TR-2004-12, Feb. 2004 Scientific Data Federation, J. Gray, A. S. Szalay, The Grid 2: Blueprint for a New Computing Infrastructure, I. Foster, C. Kesselman, eds, Morgan Kauffman, 2003, pp 95-108. Data Mining the SDSS SkyServer Database, J. Gray, A.S. Szalay, A. Thakar, P. Kunszt, C. Stoughton, D. Slutz, J. vandenBerg, Distributed Data & Structures 4: Records of the 4th International Meeting, pp 189-210, W. Litwin, G. Levy (eds),, Carleton Scientific 2003, ISBN 1-894145-13-5, also MSR-TR-2002-01, Jan. 2002 Petabyte Scale Data Mining: Dream or Reality?, Alexander S. Szalay; Jim Gray; Jan vandenBerg, SIPE Astronomy Telescopes and Instruments, 22-28 August 2002, Waikoloa, Hawaii, MSR-TR-2002-84 Online Scientific Data Curation, Publication, and Archiving, J. Gray; A. S. Szalay; A.R. Thakar; C. Stoughton; J. vandenBerg, SPIE Astronomy Telescopes and Instruments, 22-28 August 2002, Waikoloa, Hawaii, MSR-TR-2002-74 The World Wide Telescope: An Archetype for Online Science, J. Gray; A. Szalay,, CACM, Vol. 45, No. 11, pp 50-54, Nov. 2002, MSR TR 2002-75, The SDSS SkyServer: Public Access To The Sloan Digital Sky Server Data, A. S. Szalay, J. Gray, A. Thakar, P. Z. Kunszt, T. Malik, J. Raddick, C. Stoughton, J. vandenBerg:, ACM SIGMOD 2002: 570-581 MSR TR 2001 104. The World Wide Telescope, A.S., Szalay, J., Gray, Science, V.293 pp. 2037-2038. 14 Sept 2001. MS-TR-2001-77 Designing & Mining Multi-Terabyte Astronomy Archives: Sloan Digital Sky Survey, A. Szalay, P. Kunszt, A. Thakar, J. Gray, D. Slutz, P. Kuntz, June 1999, ACM SIGMOD 2000, MS-TR-99-30, 54