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The Data Avalanche
Jim GrayMicrosoft Research
[email protected]://research.microsoft.com/~Gray
Talk atUniversity of Tokyo, Japan
October 2005
NumbersTeraBytes and Gigabytes are BIG!
• Mega – a house in san francisco
• Giga – a very rich person
• Tera – ~ The Bush national debt
• Peta – more than all the money in the world
• A Gigabyte: the Human Genome
• A Terabyte: 150 mile long shelf of books.
OutlineHistorical trends imply that in 20 years:1. we can store everything in cyberspace.
The personal petabyte.2. computers will have natural interfaces
speech recognition/synthesisvision, object recognition beyond OCR
Implications1. The information avalanche will only get
worse. 2. The user interface will change:
less typing, more writing, talking, gesturing,more seeing and hearing
3. Organizing, summarizing, prioritizinginformation is a key technology.
We are here
Yotta
Zetta
Exa
Peta
Tera
Giga
Mega
Kilo
How much information is there?
• Soon everything can be recorded and indexed
• Most bytes will never be seen by humans.
• Data summarization, trend detection anomaly detection are key technologies
See Mike Lesk: How much information is there: http://www.lesk.com/mlesk/ksg97/ksg.html
See Lyman & Varian:
How much informationhttp://www.sims.berkeley.edu/research/projects/how-much-info/
Yotta
Zetta
Exa
Peta
Tera
Giga
Mega
KiloA BookA Book
.Movie
All books(words)
All Books MultiMedia
Everything!
Recorded
A PhotoA Photo
24 Yecto, 21 zepto, 18 atto, 15 femto, 12 pico, 9 nano, 6 micro, 3 milli
Things Have Changed
• IBM 305 RAMAC
• 10 MB disk
• ~1M$ (y2004 $)
1956
The Next 50 years will see MORE CHANGE ops/s/$ Had Three Growth Curves 1890-1990
1890-1945Mechanical
Relay
7-year doubling
1945-1985Tube, transistor,..
2.3 year doubling
1985-2004Microprocessor
1.0 year doubling1.E-06
1.E-03
1.E+00
1.E+03
1.E+06
1.E+09
1880 1900 1920 1940 1960 1980 2000
doubles every 7.5 years
doubles every 2.3 years
doubles every 1.0 years
ops per second/$
Combination of Hans Moravac + Larry Roberts + Gordon Bell WordSize*ops/s/sysprice
Constant Cost or Constant Function?• 100x improvement per decade
• Same function 100x cheaper
• 100x more function for same price
Mainframe
Mini
Workstation
PDA
SMP Constellation Cluster
SMP Constellation
Graphics/storage
Camera/browser
Constant PriceLower Price – New Category
Growth Comes From NEW Apps• The 10M$ computer of 1980 costs 1k$ today• If we were still doing the same things,
IT would be a 0 B$/y industry• NEW things absorb the new capacity
The Surprise-Free Futurein 20 years.
• 10,000x more power for same price– Personal supercomputer– Personal petabyte stores
• Same function for 10,000x less cost.– Smart dust --the penny PC? – The 10 peta-op computer (for 1,000$).
10,000x would change things
• Human computer interface– Decent computer vision– Decent computer speech recognition– Decent computer speech synthesis
• Vast information stores
• Ability to search and abstract the stores.
How Good is HCI Today?• Surprisingly good.
– Demo of making faces http://research.microsoft.com/research/pubs/view.aspx?pubid=290
– Demo of speech synthesis• Daisy, Hal• Synthetic voice
– Speech recognition is improving fast, – Vision getting better– Pen computing finally a reality.– Displays improving fast (compared to last 30 years)
OutlineHistorical trends imply that in 20 years:1. we can store everything in cyberspace.
The personal petabyte.2. computers will have natural interfaces
speech recognition/synthesisvision, object recognition beyond OCR
Implications1. The information avalanche will only get
worse. 2. The user interface will change:
less typing, more writing, talking, gesturing,more seeing and hearing
3. Organizing, summarizing, prioritizinginformation is a key technology.
We are here
Yotta
Zetta
Exa
Peta
Tera
Giga
Mega
Kilo
How much information is there?
• Almost everything is recorded digitally.
• Most bytes are never seen by humans.
• Data summarization, trend detection anomaly detection are key technologies
See Mike Lesk: How much information is there: http://www.lesk.com/mlesk/ksg97/ksg.html
See Lyman & Varian:
How much informationhttp://www.sims.berkeley.edu/research/projects/how-much-info/
Yotta
Zetta
Exa
Peta
Tera
Giga
Mega
KiloA BookA Book
.Movie
All books(words)
All Books MultiMedia
Everything!
Recorded
A PhotoA Photo
And >90% in Cyberspace Because:
Low rentmin $/byte
Shrinks timenow or later
Shrinks spacehere or there
Automate processingknowbots
Point-to-Point OR Broadcast
Imm
edia
te O
R T
ime
Del
ayed
LocateProcessAnalyzeSummarize
MyLifeBits The guinea pig• Gordon Bell is digitizing his life• Has now scanned virtually all:
– Books written (and read when possible)– Personal documents (correspondence, memos, email, bills, legal,0…) – Photos– Posters, paintings, photo of things (artifacts, …medals, plaques)– Home movies and videos– CD collection– And, of course, all PC files
• Recording: phone, radio, TV, web pages… conversations• Paperless throughout 2002. 12” scanned, 12’ discarded.• Only 30GB Excluding videos• Video is 2+ TB and growing fast
Capture and encoding
I mean everything
25Kday life ~ Personal Petabyte
0.001
0.01
0.1
1.
10.
100.
1000.
TB
Msgs webpages
Tifs Books jpegs 1KBpssound
music Videos
Lifetime Storage 1PB
Will anyone look at web pages in 2020? Probably new modalities & media will dominate then.
Challenges
• Capture: Get the bits in
• Organize: Index them
• Manage: No worries about loss or space
• Curate/ Annotate: atutomate where possible
• Privacy: Keep safe from theft.
• Summarize: Give thumbnail summaries
• Interface: how ask/anticipate questions
• Present: show it in understandable ways.
MemexAs We May Think, Vannevar Bush, 1945
“A memex is a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility”
“yet if the user inserted 5000 pages of material a day it would take him hundreds of years to fill the repository, so that he can be profligate and enter material freely”
Too much storage?Try to fill a terabyte in a year
Item Items/TB Items/day
300 KB JPEG 3 M 9,800
1 MB Doc 1 M 2,900
1 hour 256 kb/s MP3 audio
9 K 26
1 hour 1.5 Mbp/s MPEG video
290 0.8
Petabyte volume has to be some form of video.
How Will We Find Anything?• Need Queries, Indexing, Pivoting,
Scalability, Backup, Replication,Online update, Set-oriented access
• If you don’t use a DBMS, you will implement one!
• Simple logical structure: – Blob and link is all that is inherent– Additional properties (facets == extra tables)
and methods on those tables (encapsulation) • More than a file system • Unifies data and meta-data
SQL ++SQL ++DBMSDBMS
Photos
Searching: the most useful app?
• Challenge: What questions for useful results?
• Many ways to present answers
•
Detail view
Resource explorerAncestor (collections), annotations, descendant
& preview panes turned on
Synchronized timelines with histogram guide
Value of media depends on annotations
• “Its just bits until it is annotated”
System annotations provide base level of value
• Date 7/7/2000
Tracking usage – even better
• Date 7/7/2000. Opened 30 times, emailed to 10 people (its valued by the user!)
Get the user to say a little something is a big jump
• Date 7/7/2000. Opened 30 times, emailed to 10 people. “BARC dim sum intern farewell Lunch”
Getting the user to tell a story is the ultimate in media value
• A story is a “layout” in time and space• Most valuable content (by selection, and by being well annotated)• Stories must include links to any media they use (for future navigation/search –
“transclusion”).• Cf: MovieMaker; Creative Memories PhotoAlbums
Dapeng was an Dapeng was an intern at BARC intern at BARC for the summer for the summer of 2000of 2000
We took him to We took him to lunch at our lunch at our favorite Dim Sum favorite Dim Sum place to say place to say farewellfarewell
At table L-R: Dapeng, Gordon, Tom, Jim, Don, At table L-R: Dapeng, Gordon, Tom, Jim, Don, Vicky, Patrick, JimVicky, Patrick, Jim
Value of media depends on annotations
• Auto-annotate whenever possible e.g. GPS cameras
• Make manual annotation as easy as possible. XP photo capture, voice, photos with voice, etc
• Support gang annotation• Make stories easy
“Its just bits until it is annotated”
Dapeng was Dapeng was an intern at an intern at BARC for the BARC for the summer of summer of 20002000
We took We took him to him to lunch at our lunch at our favorite favorite Dim Sum Dim Sum place to say place to say farewellfarewell
At table L-R: Dapeng, Gordon, Tom, At table L-R: Dapeng, Gordon, Tom, Jim, Don, Vicky, Patrick, JimJim, Don, Vicky, Patrick, Jim
80% of data is personal / individual. But, what about the other 20%?
• Business– Wall Mart online: 1PB and growing….– Paradox: most “transaction” systems < 1 PB.– Have to go to image/data monitoring for big data
• Government– Government is the biggest business.
• Science– LOTS of data.
CERN Tier 0
Instruments: CERN – LHCPeta Bytes per Year
Looking for the Higgs Particle
• Sensors: 1000 GB/s (1TB/s ~ 30 EB/y)
• Events 75 GB/s
• Filtered 5 GB/s
• Reduced 0.1 GB/s~ 2 PB/y
• Data pyramid: 100GB : 1TB : 100TB : 1PB : 10PB
Information Avalanche• Both
– better observational instruments and – Better simulations are producing a data avalanche
• Examples– Turbulence: 100 TB simulation
then mine the Information – BaBar: Grows 1TB/day
2/3 simulation Information 1/3 observational Information
– CERN: LHC will generate 1GB/s10 PB/y
– VLBA (NRAO) generates 1GB/s today– NCBI: “only ½ TB” but doubling each year, very rich dataset.– Pixar: 100 TB/Movie
Image courtesy of C. Meneveau & A. Szalay @ JHU
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
The Big PictureExperiments &
Instruments
Simulationsfacts
facts
answers
questions
• Data ingest • Managing a petabyte• Common schema• How to organize it?• How to reorganize it• How to coexist with others
• Query and Vis tools • Support/training• Performance
– Execute queries in a minute – Batch query scheduling
?The Big Problems
Literature
Other Archives facts
facts
FTP - GREP • Download (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 ~3,000 disks
• At some point we need indices to limit searchparallel data search and analysis
• This is where databases can help
• Next generation technique: Data Exploration– Bring the analysis to the data!
The Speed Problem• Many users want to search the whole DB
ad hoc queries, often combinatorial• Want ~ 1 minute response• Brute force (parallel search):
– 1 disk = 50MBps => ~1M disks/PB ~ 300M$/PB
• Indices (limit search, do column store)– 1,000x less equipment: 1M$/PB
• Pre-compute answer– No one knows how do it for all questions.
Next-Generation 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? – Relax notion of optimal
(data is fuzzy, answers are approximate)– Don’t assume infinite computational resources or memory
• Combination of statistics & computer science
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.
OutlineHistorical trends imply that in 20 years:1. we can store everything in cyberspace.
The personal petabyte.2. computers will have natural interfaces
speech recognition/synthesisvision, object recognition beyond OCR
Implications1. The information avalanche will only get
worse. 2. The user interface will change:
less typing, more writing, talking, gesturing,more seeing and hearing
3. Organizing, summarizing, prioritizinginformation is a key technology.
We are here
Yotta
Zetta
Exa
Peta
Tera
Giga
Mega
Kilo
The Evolution of Science• Observational Science
– Scientist gathers data by direct observation– Scientist analyzes data
• Analytical Science – Scientist builds analytical model– Makes predictions.
• Computational Science – Simulate analytical model– Validate model and makes predictions
• Data Exploration Science Data captured by instrumentsOr data generated by simulator– Processed by software– Placed in a database / files– Scientist analyzes database / files
e-Science
• Data captured by instrumentsData captured by instrumentsOr data generated by simulatorOr data generated by simulator
• Processed by softwareProcessed by software
• Placed in a files or databasePlaced in a files or database
• Scientist analyzes files / databaseScientist analyzes files / database
• Virtual laboratoriesVirtual laboratories– Networks connecting e-ScientistsNetworks connecting e-Scientists– Strong support from funding agenciesStrong support from funding agencies
• Better use of resourcesBetter use of resources
– Primitive todayPrimitive today
e-Science is Data Mininge-Science is Data Mining• There are LOTS of data
– people cannot examine most of it.– Need computers to do analysis.
• Manual or Automatic Exploration– Manual: person suggests hypothesis,
computer checks hypothesis
– Automatic: Computer suggests hypothesisperson evaluates significance
• Given an arbitrary parameter space:– Data Clusters– Points between Data Clusters– Isolated Data Clusters– Isolated Data Groups– Holes in Data Clusters– Isolated Points
Nichol et al. 2001Nichol et al. 2001Slide courtesy of and adapted from Robert Brunner @ Slide courtesy of and adapted from Robert Brunner @
CalTechCalTech..
TerraServer/TerraServicehttp://terraService.Net/
• US Geological Survey Photo (DOQ) & Topo (DRG) images online.
• On Internet since June 1998
• Operated by Microsoft Corporation
• Cross Indexed with– Home sales, – Demographics, – Encyclopedia
• A web service• 20 TB data source• 10 M web hits/day
USGS Image Data
• Digital OrthoQuads– 18 TB, 260,000 files
uncompressed– Digitized aerial imagery– 88% coverage
conterminous US – 1 meter resolution– < 10 years old
• Digital Raster Graphics– 1 TB compressed TIFF, 65,000
files– Scanned topographic maps– 100% U.S. coverage– 1:24,000, 1:100,000 and
1:250,000 scale maps– Maps vary in age
User Interface ConceptDisplay Imagery:
316 m 200 x 200 pixel images7 level image pyramidResolution 1 meter/pixel to 64 meter/pixel
Navigation Tools: 1.5 m place names“Click-on” Coverage mapLongitude and Latitude searchU.S. Address Search
External Geo-Spatial Links to:USGS On-line Stream GaugesHome Advisor DemographicsHome Advisor Real EstateEncarta ArticlesSteam flow gauges
Concept: User navigates an ‘almost seamless’ image of earth
Buttons to pan NW, N, NE, W, E, SW, S, SE
Click on image to zoom in
Links to switch betweenTopo, Imagery, and Relief data
Links to Print, Download andview meta-data information
Terra Service New Things• A popular web service
– Exactly the map you want.
• Dynamic Map Re-projection– UTM to Geographic projection– Dynamic texture mapping?
• New Data– 1 foot resolution natural
color imagery– Census Tiger data
• Lights Out Management– MOM– Auto-backup / restore on drive failure
“Urban Area” Data
““Redundant Bunch 1”Redundant Bunch 1”
Microsoft Campus at 4 meterMicrosoft Campus at 4 meter resolution resolution
Ball field at .25 meterBall field at .25 meter resolutionresolution
TerraServer Becomes a Web ServiceTerraServer.net -> TerraService.Net
• Web server is for people.• Web Service is for programs
– The end of screen scraping– No faking a URL:
pass real parameters.– No parsing the answer:
data formatted into your address space.
• Hundreds of users but a specific example:– US Department of Agriculture
TerraServer Web Services
• Get image meta-data • Query TS Gazetteer• Retrieve TS ImageTiles • Projection conversions
• Web Map Client– OpenGIS “like” – Landmarks layered on
TerraServer imagery
• Geo-coded data of well-known objects (points), e.g. Schools, Golf Courses, Hospitals, etc.
• Polygons of well-known objects (shapes), e.g. Zip Codes, Cities, etc
• Fat Map Client– Visual Basic / C#
Windows Form– Access Web Services for
all data
Terra-Tile-Service Landmark-Service
http://terraservice.net
Sample Apps
Web Services • Web SERVER:
– Given a url + parameters – Returns a web page (often dynamic)
• Web SERVICE:– Given a XML document (soap msg)– Returns an XML document– 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
Yourprogram
DataIn your address
space
Web Service
soap
object
in
xml
Yourprogram Web
Server
http
Web
page
KVM / IPKVM / IP
TerraServer Hardware • Storage Bricks
– “White-box commodity servers”– 4tb raw / 2TB Raid1 SATA storage– Dual Hyper-threaded Xeon 2.4ghz, 4GB RAM
• Partitioned Databases (PACS – partitioned array)– 3 Storage Bricks = 1 TerraServer data – Data partitioned across 20 databases– More data & partitions coming
• Low Cost Availability– 4 copies of the data
• RAID1 SATA Mirroring• 2 redundant “Bunches”
– Spare brick to repair failed brick 2N+1 design
– Web Application “bunch aware”• Load balances between redundant databases• Fails over to surviving database on failure
• ~100K$ capital expense.
Virtual Observatoryhttp://www.astro.caltech.edu/nvoconf/
http://www.voforum.org/
• 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.
Why Astronomy Data?•It has no commercial value
–No privacy concerns–Can freely share results with others–Great for experimenting with algorithms
•It is real and well documented–High-dimensional data (with confidence intervals)–Spatial data–Temporal data
•Many different instruments from many different places and many different times•Federation is a goal•The questions are interesting
–How did the universe form?
•There is a lot of it (petabytes)
IRAS 100
ROSAT ~keV
DSS Optical
2MASS 2
IRAS 25
NVSS 20cm
WENSS 92cm
GB 6cm
Time and Spectral DimensionsThe Multiwavelength Crab Nebulae
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.Slide courtesy of Robert Brunner @ CalTech.
Crab star 1053 AD
SkyServer.SDSS.org• A modern archive
– 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 interface via Emacs– Popular -- 1% of Terraserver – Cloned by other surveys (a template design) – Web services are core of it.
Demo of SkyServer
• Shows standard web server
• Pixel/image data
• Point and click
• Explore one object
• Explore sets of objects (data mining)
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 a web service– Schema: documents the data– Methods on objects (queries)
• Scientists get “personalized” extracts
• Uniform access to multiple Archives– A common global schema
SkyQueryA Prototype WWT
• Started with SDSS data and schema• Imported12 other datasets
into that spine schema.(a day per dataset plus load time)
• Unified them with a portal • Implicit spatial join among the datasets.• All built on Web Services
– Pure XML– Pure SOAP– Used .NET toolkit
Federation: SkyQuery.Net• Combine 4 archives initially
• Just added 10 more
• Send query to portal, portal joins data from archives.
• Problem: want to do multi-step data analysis (not just single query).
• Solution: Allow personal databases on portal
• Problem: some queries are monsters
• Solution: “batch schedule” on portal server, Deposits answer in personal database.
2MASS
INT
SDSS
FIRST
SkyQueryPortal
ImageCutout
SkyQuery Structure• Each SkyNode publishes
– Schema Web Service– Database Web Service
• Portal is – Plans Query (2 phase) – Integrates answers– Is itself a web service
SkyQuery: http://skyquery.net/• Distributed Query tool using a set of web services• Four astronomy archives from
Pasadena, Chicago, Baltimore, Cambridge (England).• Feasibility study, built in 6 weeks
– Tanu Malik (JHU CS grad student) – Tamas Budavari (JHU astro postdoc)– With help from Szalay, Thakar, Gray
• Implemented in C# and .NET• 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
SkyNode Basic Web Services• Metadata information about resources
– Waveband– Sky coverage– Translation of names to universal dictionary (UCD)
• Simple search patterns on the resources– Cone Search– Image mosaic– Unit conversions
• Simple filtering, counting, histogramming• On-the-fly recalibrations
Portals: Higher Level Services• Built on Atomic Services• Perform more complex tasks• Examples
– Automated resource discovery– Cross-identifications– Photometric redshifts– Outlier detections– Visualization facilities
• Goal:– Build custom portals in days from existing building blocks
(like today in IRAF or IDL)
Open SkyQuery
• SkyQuery being adopted by AstroGrid as reference implementation for OGSA-DAI(Open Grid Services Architecture, Data Access and Integration).
• SkyNode basic archive objecthttp://www.ivoa.net/twiki/bin/view/IVOA/SkyNode
• SkyQuery Language (VoQL) is evolving.http://www.ivoa.net/twiki/bin/view/IVOA/IvoaVOQL
The Registry• UDDI seemed inappropriate
– Complex – Irrelevant questions– Relevant questions missing
• Evolved Dublin Core– Represent Datasets, Services, Portals– Needs to be machine readable– Federation (DNS model)– Push & Pull: register then harvest
• http://www.ivoa.net/twiki/bin/view/IVOA/IvoaResReg
Unified Definitions• Universal Content Definitions
http://vizier.u-strasbg.fr/doc/UCD.htx
– Collated all table heads from all the literature– 100,000 terms reduced to ~1,500– Rough consensus that this is the right thing.– Refinement in progress as people use UCDs
• Defines – Units:
• gram, radian, second, janski...
– Semantic Concepts / Metrics • Std error, Chi2 fit, magnitude, flux @ passband, velocity,
Classes and Methods
• First Class: VO tablehttp://www.us-vo.org/VOTable/
– Represents an answer set in XML• Defined by an XML Schema (XSD) • Metadata (in terms of UCDs)• Data representation (numbers and text)
– First method• Cone Search: Get objects in this cone
http://voservices.org/cone/
Yourprogram
DataIn your address
space
Web Service
soap
object
in xml
Provenance• Most data will be derived.• To do science,
need to trace derived data back to source.• So programs and inputs must be registered.• Must be able to re-run them.• Example: Space Telescope Calibrated Data
– Run on demand– Can specify software version (to get old answers)
• Scientific Data Provenance and Curation are largely unsolved problems (some ideas but no science).
Other Classes• Space-Time class
– http://hea-www.harvard.edu/~arots/nvometa/STCdoc.pdf
• Image Class (returns pixels)– SdssCutout– Simple Image Access Protocol
http://bill.cacr.caltech.edu/cfdocs/usvo-pubs/files/ACF8DE.pdf
– HyperAtlashttp://bill.cacr.caltech.edu/usvo-pubs/files/hyperatlas.pdf
• Spectral – Simple Spectral Access Protocol – 500K spectra available at http://voservices.net/wave
• Query Services– ADQL and SkyNode http://skyservice.pha.jhu.edu/develop/vo/adql/– And http://SkyQuery.Net
• Registry: – see below
Yourprogram
DataIn your address
space
Web Service
soap
object
in xml
Object Model• General acceptance of XML • Recent acceptance of XML Schema
(XSD over DTD)
• Wait-and-See about SOAP/WSDL/…– “ Web Services are just Corba with angle
brackets.”
– FTP is good enough for me.
• Personal opinion:– Web Services are much more than
“Corba + <>”– Huge focus on interop– Huge focus on integrated tools
• But the community says “Show me!”– Many technologists convinced,
but not yet the astronomers
Yourprogram
DataIn your address
space
Web Service
soap
object
in
xml
Yourprogram Web
Server
http
Web
page
Data Sources• Literature online and cross indexed
– Simbad, ADS, NED,http://simbad.u-strasbg.fr/Simbad, http://adswww.harvard.edu/, http://nedwww.ipac.caltech.edu/
• Many curated archives online– FIRST, DPOSS, 2MASS, USNO, IRAS, SDSS, VizeR,…– Typically files with English meta-data and some programs
• Groups, Researchers, Amateurs Publish– Datasets online in various formats– Data publications are ephemeral (may disappear) – Many have unknown provenance
• Documentation varies; some good and some none.
The WWT ComponentsOutline• Data Sources
– Literature– Archives
• Unified Definitions– Units, – Semantics/Concepts/Metrics,
Representations, – Provenance
• Object model• Classes and methods• Portals• WWT is a poster child for
the Data Grid.
What we learned• Astro is a community of 10,000 • Homogenous & Cooperative• If you can’t do it for Astro,
do not bother with 3M bio-info.• Agreement
– Takes time – Takes endless meetings
• Big problems are non-technical– Legacy is a big problem.
• Plumbing and tools are thereBut…– What is the object model?– What do you want to save?– How document provenance?
2MASS
INT
SDSS
FIRST
SkyQueryPortal
ImageCutout
MyDB added to SkyQuery• Let users add personal DB
1GB for now.• Use it as a workbook.• Online and batch queries.
• Moves analysis to the data• Users can cooperate
(share MyDB)• Still exploring this
MyDB
The Big PictureExperiments &
Instruments
Simulationsfacts
facts
answers
questions
• Data ingest • Managing a petabyte• Common schema• How to organize it?• How to reorganize it• How to coexist with others
• Query and Vis tools • Support/training• Performance
– Execute queries in a minute – Batch query scheduling
?The Big Problems
Literature
Other Archives facts
facts
