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Where The Rubber Meets the Sky Giving Access to Science Data Jim Gray Microsoft Where The Rubber Meets the Sky Giving Access to Science Data Jim Gray Microsoft Research [email protected] com Http: //research. Microsoft. com/~Gray Alex Szalay Johns Hopkins University [email protected] edu Talk at 16 th International Conference on Scientific and Statistical Database Management June 2004, Santorini, Greece 1

Promised Abstract: Historically, scientists gatherer and analyzed their own data. But technology has created Promised Abstract: Historically, scientists gatherer and analyzed their own data. But technology has created functional specialization where some scientists gather or generate data, and others analyze it. Technology allows us to easily capture vast amounts of empirical data and to generate vast amounts of simulated data. Technology also allows us to store these bytes almost indefinitely. But there are few tools to organize scientific data for easy access and query, few tools to curate the data, and few tools to federate science archives. Domain scientists, notably NCBI and the Virtual Observatory, are making heroic efforts to address these problems. But this is a generic problem that cuts across all scientific disciplines. It requires a coordinated effort by the computer science community to build generic tools that will help all the sciences. Our current database products are a start, but much more is needed. Actual Abstract: I have been working with Alex Szalay and other astronomers for the last 6 years trying to apply DB technology to science problems. These are some lessons I learned. 2

Outline • New Science – X-Info for all fields X – WWT as an 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 & fa Instruments cts Other Archives facts Literature facts ? questions answers s Simulations t fac 3

New Science Paradigms • Thousand years ago: science was empirical describing natural phenomena • 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 (e. Science) 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 4

The Virtual Observatory • Premise: most data is (or could be online) • The 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. 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 25 m 2 MASS 2 m – No privacy concerns, freely share results with others DSS Optica – Great for experimenting with algorithms • It is real and well documented – High-dimensional (with confidence intervals) – Spatial, temporal IRAS 100 m • Diverse and distributed – Many different instruments from many different places and many different times WENSS 92 cm NVSS 20 cm • The community wants to share the data • There is a lot of it (soon petabytes) 6 ROSAT ~ke. V GB 6 cm

The Big Picture Experiments & Instruments fac Other Archives facts Literature ts facts ts The Big Picture Experiments & Instruments fac Other Archives facts Literature ts facts ts Simulations fac ? questions answers The Big Problems • • • 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 7

What X-info Needs from us (cs) (not drawn to scale) Miners Scientists Data Mining What X-info Needs from us (cs) (not drawn to scale) Miners Scientists Data Mining Algorithms Plumbers Database To store data Execute Queries Question & Answer Visualization Tools 8

Experiment Budgets ¼…½ Software for • Instrument scheduling • Instrument control • Data gathering Experiment Budgets ¼…½ 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 • … 9 Simulation (computational science) are > ½ software

Data Access Hitting a Wall Current science practice based on data download (FTP/GREP) Will 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$) … 2 days and 1 K$ … 3 years and 1 M$ • Oh!, and 1 PB ~5, 000 disks • At some point you need indices to limit search parallel data search and analysis • This is where databases can help 10

Next-Generation Data Analysis • Looking for – Needles in haystacks – the Higgs particle 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 log. N • A way out? – Relax optimal notion (data is fuzzy, answers are approximate) – Don’t assume infinite computational resources or memory 11 • Requires combination of statistics & computer science

Smart Data: Unifying DB and Analysis • There is too much data to move 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 Move Mohamed to the mountain, not the mountain to Mohamed. – Examples • Statistical sampling and analysis • Temporal and spatial indexing • Pixel processing • Automatic parallelism • Auto (re)organize • Scalable to Petabyte datasets 12

Outline • New Science • Working cross disciplines – How to help? – 20 Outline • New Science • Working cross disciplines – How to help? – 20 questions – WWT example – Alternative: CS Process Models • Data Demographics and Data Handling • Curation Experiments & fac Instruments ts Other Archives facts Literature facts ? questions answers s Simulations t fac 13

How to Help? • Can’t learn the discipline before you start (takes 4 years. 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

Working Cross-Culture A Way to Engage With Domain Scientists • Communicate in terms of Working Cross-Culture A Way to Engage With Domain Scientists • Communicate in terms of scenarios • Work on a problem that gives 100 x 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

Working Cross-Culture -- 20 Questions: A Way to Engage With Domain Scientists • Astronomers 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

The 20 Queries Q 1: Find all galaxies without unsaturated pixels within 1' of The 20 Queries Q 1: Find all galaxies without unsaturated pixels within 1' of a given point of ra=75. 327, dec=21. 023 Q 2: Find all galaxies with blue surface brightness between and 23 and 25 mag per square arcseconds, and 100. 75. Q 4: 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. Q 5: Find all galaxies with a de. Vaucouleours profile (r¼ falloff of intensity on disk) and the photometric colors consistent with an elliptical galaxy. The de. Vaucouleours profile Q 6: Find galaxies that are blended with a star, output the deblended galaxy magnitudes. Q 7: Provide a list of star-like objects that are 1% rare. Q 8: Find all objects with unclassified spectra. Q 9: Find quasars with a line width >2000 km/s and 2. 540Å (Ha is the main hydrogen spectral line. ) Q 11: Find all elliptical galaxies with spectra that have an anomalous emission line. Q 12: Create a grided count of galaxies with u-g>1 and r<21. 5 over 600. 1. Scan for stars that have a secondary object (observed at a different time) and compare their magnitudes. Q 15: Provide a list of moving objects consistent with an asteroid. Q 16: Find all objects similar to the colors of a quasar at 5. 5

Two kinds of SDSS data in an SQL DB (objects and images all in Two kinds of SDSS data in an SQL DB (objects and images all in DB) 300 M Photo Objects ~ 400 attributes 10 B rows overall 400 K Spectra with ~30 lines/ Spectrum 100 M rows 18

An easy one: Q 7: Provide a list of star-like objects that are 1% An easy one: Q 7: 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(*) 19

Then What? 1999. 20 Queries were a way to engage – – Needed spatial 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. Sky. Query federation evolved to 2004. Now focused on spatial data library. Conversion to OR DB (put analysis in DB). 20

Alternate Model • Many sciences are becoming information sciences • Modeling systems needs new 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. 21

Outline • New Science • Working cross disciplines • Data Demographics and Data Handling Outline • New Science • Working cross disciplines • Data Demographics and Data Handling – Exponential growth – Data Lifecycle – Versions – Data inflation – Year 5 – Overprovision by 6 x Experiments & fac Instruments ts – Data Loading – Regression Tests Other Archives facts – Statistical subset Literature ts • Curation fac ? Simulations questions answers 22

Information Avalanche • In science, industry, government, …. – better observational instruments and – 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 – Ba. Bar: Grows 1 TB/day 2/3 simulation Information 1/3 observational Information – CERN: LHC will generate 1 GB/s. ~10 PB/y – VLBA (NRAO) generates 1 GB/s today – Pixar: 100 TB/Movie Ba. Bar, Stanford P&E Gene Sequencer From http: //www. genome. uci. edu/ • New emphasis on informatics: – Capturing, Organizing, Summarizing, Analyzing, Visualizing 23 Space Telescope

Q: Where will the Data Come From? A: Sensor Applications • Earth Observation – 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 – ~1 E 8 video cameras @ 1 E 5 MBps 10 TB/s 100 EB/y filtered? ? ? • Airplane Engines – 1 GB sensor data/flight, – 100, 000 engine hours/day – 30 PB/y • Smart Dust: ? ? EB/y http: //robotics. eecs. berkeley. edu/~pister/Smart. Dust/ http: //www-bsac. eecs. berkeley. edu/~shollar/macro_motes/macromotes. html 24

Instruments: CERN – LHC Peta Bytes per Year Looking for the Higgs Particle • 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: 100 GB : 1 TB : 100 TB : 1 PB : 10 PB 25

Like all sciences, Astronomy Faces an Information Avalanche • Astronomers have a few hundred Like all sciences, Astronomy Faces an Information Avalanche • Astronomers have a few hundred TB now – 1 pixel (byte) / sq arc second ~ 4 TB – Multi-spectral, temporal, … → 1 PB • 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 26

Moore’s Law in Proteomics Courtesy of Peter Berndt, Roche Center for Medical Genomics (RCMG): Moore’s Law in Proteomics Courtesy of Peter Berndt, Roche Center for Medical Genomics (RCMG): number of mass-spectra acquired for proteomics doubled every year since first mass spectrometer deployed. R 2=0. 96 27

Data Lifecycle • Raw data → primary data → derived data • Data has 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 other data pipeline Level 2 derived pipeline other data 28

Data Inflation – Data Pyramid Level 2 Level 1 A Grows X TB/year ~. Data Inflation – Data Pyramid Level 2 Level 1 A Grows X TB/year ~. 4 X TB/y compressed (level 1 A in NASA terms) Derived data products ~10 x smaller But there are many. L 2≈L 1 • 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: L 2 ≥ L 1 Level 1 A 4 editions of 4 Level 2 products E 4 E 3 time E 2 E 1 4 editions of level 1 A 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 level 2 data more than doubles the total storage volume. 29

The Year 5 Problem • Data arrives at R bytes/year • New Storage & 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 6 x Over-Power slide next 30

6 x Over-Power Ratio • If you think you need X raw capacity, then 6 x Over-Power Ratio • If you think you need X raw capacity, then you probably need 6 X • Reprocessing • Backup copies • Versions • … • Hardware is cheap, Your time is precious. Pub. DB 3. 6 TB DR 2 C 1. 8 TB DR 2 M 1. 8 TB DR 2 P 1. 8 TB DR 3 C 2. 4 TB DR 3 M 2. 4 TB DR 3 P 2. 4 TB 31

Data Loading • Data from outside – Is full of bugs – Is not 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! 32

Performance Prediction & Regression • Database grows exponentially • Set up response-time requirements – 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. 33

Data Subsets For Science and Development • Offer 1 GB, 10 GB, …, Full Data Subsets For Science and Development • Offer 1 GB, 10 GB, …, 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. 34

Outline • • New Science Working cross disciplines Data Demographics and Data Handling Curation Outline • • New Science Working cross disciplines Data Demographics and Data Handling Curation – Problem statement – Economics – Astro as a case in point Experiments & fac Instruments ts Other Archives facts Literature facts ? questions answers s Simulations t fac 35

Problem Statement • Once published, scientific data needs to be available forever, so that 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, 36

The Core Problem: No Economic Model • The archive user is not yet born. 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) 37

Publishing Data Roles Traditional Emerging Authors Scientists Collaborations Publishers Journals Project web site Curators Publishing Data Roles Traditional Emerging Authors Scientists Collaborations Publishers Journals Project web site Curators Libraries Data+Doc Archives Digital Archives Consumers Scientists 38

Changing Roles • Exponential growth: – – Projects last at least 3 -5 years 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 39

What SDSS is Doing: Capture the Bits (preserve the primary data) • Best-effort documenting 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 (~ 5 TB today and so ~5 k$ 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 40 organize/search all this metadata.

Schema (aka metadata) • Everyone starts with the same schema <stuff/> Then the start Schema (aka metadata) • Everyone starts with the same schema 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. 41

Summary • New Science – X-Info for all fields X – WWT as an 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 6 x – Data Loading – Regression Tests – Statistical subset • Curation – Problem statement – Economics – Astro as a case in point 42

Call to Action • X-info is emerging. • Computer Scientists can help in many 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 43

References http: //Sky. Server. SDSS. org/ http: //research. microsoft. com/pubs/ http: //research. microsoft. com/Gray/SDSS/ References http: //Sky. Server. SDSS. org/ http: //research. microsoft. com/pubs/ http: //research. microsoft. com/Gray/SDSS/ (download personal Sky. Server) http: //research. microsoft. com/Gray 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 Sky. Server Database, J. Gray, A. S. Szalay, A. Thakar, P. Kunszt, C. Stoughton, D. Slutz, J. vanden. Berg, Distributed Data & Structures 4: Records of the 4 th 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 vanden. Berg, 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. vanden. Berg, 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 Sky. Server: 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. vanden. Berg: , 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, 44

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How to Publish Data: Web Services • Web SERVER: – Given a url + How to Publish Data: Web Services • Web SERVER: – Given a url + parameters – Returns a web page (often dynamic) • Web SERVICE: • Your h t program tp b We e pag Web Server – Given a XML document (soap msg) – Returns an XML document (with schema) – Tools make this look like an RPC. Your s • F(x, y, z) returns (u, v, w) o program ap Web – Distributed objects for the web. Service – + naming, discovery, security, . . t jec l Data ob m in x In your Internet-scale address distributed computing space 47

Global Federations • Massive datasets live near their owners: – Near the instrument’s software 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 Fede ratio n 48

The Sloan Digital Sky Survey • Goal – Create the most detailed map of The Sloan Digital Sky Survey • Goal – Create the most detailed map of the Northern Sky to-date • 2. 5 m telescope – 3 degree field of view • Two surveys in one The University of Chicago Princeton University The Johns Hopkins University The University of Washington New Mexico State 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 – 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 49

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

Demo Sky. Server • • atlas education project Mouse in pixel space Explore an Demo Sky. Server • • atlas education project Mouse in pixel space Explore an object (record space) • Explore literature • Explore a set • Pose a new question 51

Sky. Query (http: //skyquery. net/) • Distributed Query tool using a set of web Sky. Query (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 • SELECT o. obj. Id, o. r, o. type, Allows queries like: t. obj. Id FROM SDSS: Photo. Primary o, TWOMASS: Photo. Primary 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 52

Demo Sky. Query Structure • Portal is – Plans Query (2 phase) – Integrates Demo Sky. Query Structure • Portal is – Plans Query (2 phase) – Integrates answers – Is itself a web service • Each Sky. Node publishes – Schema Web Service – Database Web Service Image Cutout SDSS Sky. Query Portal FIRST 2 MASS INT 53

My. DB: e. Science Workbench • Prototype of bringing analysis to the data • My. DB: e. Science 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 54

National Center Biotechnology Information (NCBI) A good Example • Pub. Med: – Abstracts and National Center Biotechnology Information (NCBI) A good Example • Pub. Med: – Abstracts and books and. . • Gen. Bank: – 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 55

Making Discoveries • Where are discoveries made? – At the edges and boundaries – 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, 2 MASS, DPOSS 56

Global Federations • Massive datasets live near their owners: – Near the instrument’s software 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 57

The OGIS model Data Management Producer Ingest Archive Access Consumer Administer 58 The OGIS model Data Management Producer Ingest Archive Access Consumer Administer 58

Jim’s Model of Library Science • Alexandria • Gutenberg • (Melvil) Dewey Decimal • Jim’s Model of Library Science • Alexandria • Gutenberg • (Melvil) Dewey Decimal • MARC (Henriette Avram) • Dublin Core Yes, I know there have been other things. 59

Dublin Core Elements – – – – Elements+ Title Creator Subject Description Publisher Contributor Dublin Core Elements – – – – Elements+ Title Creator Subject Description Publisher Contributor Date Type Format Identifier Source Language Coverage Rights – – – – – – – – – Audience Alternative Table. Of. Contents Abstract Created Valid Available Issued Modified Extent Medium Is. Version. Of Has. Version Is. Replaced. By Replaces Is. Required. By Requires Is. Part. Of Has. Part Is. Referenced. By References Is. Format. Of Has. Format Conforms. To Spatial Temporal Mediator Date. Accepted Date. Copyrighted Date. Submitted Educational. Level Access. Rights Bibliographic. Citation 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) ISO 639 -2 (ISO language names) RFC 1766 (Internet Language tags) URI (Uniform Resource Locator) Point (DCMI spatial point) ISO 3166 (ISO country codes) Box (DCMI rectangular area) TGN (Getty Thesaurus of Geo Names) Period (DCMI time interval) W 3 CDTF (W 3 C date/time) RFC 3066 (Language dialects) Types – – – Collection Dataset Event Image Interactive. Resouce Service Software Sound Text Physical. Object Still. Image Moving. Image 60

Access Challenges • Archived information “rusts” if it is not accessed. Access is essential. 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 also technical problems: – Access formats are different from the storage formats. • migration? • emulation? • Gold Standards? ) 61

Ingest Challenges • • Push vs Pull What are the gold standards? Automatic indexing, 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. 62

Archive Challenges • Cost of administering storage: – Presently 10 x to 100 x Archive Challenges • Cost of administering storage: – Presently 10 x to 100 x the hardware cost. • Resist attack: geographic diversity • At 1 GBps 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) 63

The Midrange Paradox • Large archives are curated – Curated by projects • Small 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. 64