Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Where The Rubber Meets the Sky Giving Access to Science Data Jim Gray Microsoft Research [email protected] Http://research.Microsoft.com/~Gray Alex Szalay Johns Hopkins University [email protected] Talk at 16th 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 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 example – Big Picture – Puzzle – Hitting the wall – Needle in haystack – Mohamed and the mountain • Working cross disciplines • Data Demographics and Data Handling • Curation Experiments & Instruments Other Archives facts Literature Simulations facts ? questions answers 3 New Science Paradigms • Thousand years ago: science was empirical describing natural phenomena • Last few hundred years: theoretical branch using models, generalizations 2 . 4G c2 a a 3 a 2 • Last few decades: a computational branch simulating complex phenomena • Today: data exploration (eScience) unify theory, experiment, and simulation using data management and statistics – Data captured by instruments Or generated by simulator – Processed by software – Scientist analyzes database / files 4 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 The Big Picture Experiments & Instruments Other Archives Literature questions facts facts ? 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? • • • 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 Science Data & Questions Data Mining Algorithms Plumbers Database To store data Execute Queries Question & Answer Visualization Tools 8 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 • … 9 Simulation (computational science) are > ½ software 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 10 Next-Generation Data Analysis • Looking for – Needles in haystacks – the Higgs particle – Haystacks: dark matter, dark energy, turbulence, ecosystem dynamics • Needles are easier than haystacks • Global statistics have poor scaling – Correlation functions are N2, likelihood techniques N3 • As data and computers grow at Moore’s Law, we can only keep up with N logN • A way out? – Relax optimal notion (data is fuzzy, answers are approximate) – Don’t assume infinite computational resources or memory 11 • Requires combination of statistics & computer science 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 Move Mohamed to the mountain, – Unify data Access & Analysis 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 questions – WWT example – Alternative: CS Process Models • Data Demographics and Data Handling • Curation Experiments & Instruments Other Archives facts Literature Simulations facts ? questions answers 13 How to Help? • Can’t learn the discipline before you start (takes 4 years.) • Can’t go native – you are a CS person not a bio,… person • Have to learn how to communicate Have to learn the language • Have to form a working relationship with domain expert(s) • Have to find problems that leverage your skills 14 Working Cross-Culture A Way to Engage With Domain Scientists • Communicate in terms of scenarios • Work on a problem that gives 100x benefit – Weeks/task vs hours/task • Solve 20% of the problem – The other 80% will take decades • Prototype • Go from working-to-working, Always have – Something to show – Clear next steps – Clear goal • Avoid death-by-collaboration-meetings. 15 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 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 photoz17 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) 300M Photo Objects ~ 400 attributes 10B rows overall 400K Spectra with ~30 lines/ Spectrum 100 M rows 18 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(*) 19 Then What? 1999. 20 Queries were a way to engage – – Needed spatial data library Needed DB design 2000. Built website to publish the data 2001. Data Loading (workflow scheduler). 2002. Pixel web service evolved to 2003. SkyQuery federation evolved to 2004. Now focused on spatial data library. Conversion to OR DB (put analysis in DB). 20 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 – 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 22 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 23 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 24 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 25 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 26 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 27 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 28 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. 29 180 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 30 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 31 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 32 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. 33 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. 34 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 35 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. 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 Libraries Data+Doc Archives Archives Archives Digital Archives Consumers Scientists Scientists 38 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 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 40 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. 41 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 42 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://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, 44 45 46 How to Publish Data: Web Services • 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 (with schema) – Tools make this look like an RPC. Your • F(x,y,z) returns (u, v, w) program – Distributed objects for the web. – + naming, discovery, security,.. Data In your Internet-scale address distributed computing space Web Service 47 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 48 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 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 – 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 SkyServer • 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 eScience lab • Interactive visual tools for data exploration 50 Demo SkyServer • • • • atlas education project Mouse in pixel space Explore an object (record space) • Explore literature • Explore a set • Pose a new question 51 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 •SELECT Allows querieso.r, like:o.type, o.objId, 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 52 Demo SkyQuery Structure • Portal is – Plans Query (2 phase) – Integrates answers – Is itself a web service • Each SkyNode publishes – Schema Web Service – Database Web Service Image Cutout SDSS INT SkyQuery Portal FIRST 2MASS 53 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 54 National Center Biotechnology Information (NCBI) A good Example • PubMed: – Abstracts and books and.. • GenBank: – All Gene sequences deposited – BLAST and other searches – Website to explore data and literature • Entrez: – unifies many databases with literature (books, journals,..) – Organizes the data 55 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(N2) • Data Federation: quadratic benefit – Federation of N archives has utility O(N2) – Possibilities for new discoveries grow as O(N2) • Current sky surveys have proven this – Very early discoveries from SDSS, 2MASS, DPOSS 56 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 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 Date Type Format Identifier Source Language Coverage Rights – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 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 60 Access Challenges • Archived information “rusts” if it is not accessed. Access is essential. • Access costs money – who pays? • Access sometimes uses IP, who pays? • There are also technical problems: – Access formats are different from the storage formats. • migration? • emulation? • Gold Standards?) 61 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 10x to 100x the hardware cost. • Resist attack: geographic diversity • At 1GBps it takes 12 days to move a PB • Store it in two (or more) places online (on disk). A geo-plex • Scrub it continuously (look for errors) • On failure, – use other copy until failure repaired, – refresh lost copy from safe copy. • Can organize the copies differently (e.g.: one by time, one by space) 63 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