Online Science The World-Wide Telescope as a Prototype

Online Science The World-Wide Telescope as a Prototype For the New Computational Science Jim Gray Microsoft Research http://research.microsoft.com/~gray Outline The World Wide Telescope Idea Data Mining the Sloan Digital Sky Survey Spherical Geometry in SQL Computational Science Traditional Empirical Science Scientist gathers data by direct observation Scientist analyzes data

Computational Science Data captured by instruments Or data generated by simulator Processed by software Placed in a database / files Scientist analyzes database / files World Wide Telescope Virtual Observatory http://www.astro.caltech.edu/nvoconf/ http://www.voforum.org/ Premise: Most data is (or could be online) So, the Internet is the worlds 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..). Its a smart telescope: links objects and data to literature on them. Whats needed? (not drawn to scale) Miners Scientists Science Data & Questions Data Mining

Algorithms Plumbers Database To store data Execute Queries Question & Answer Visualizat ion Tools Why Astronomy Data? It has no commercial value No privacy concerns Can freely share results with others

Great for experimenting with algorithms IRAS 25 2MASS 2 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 DSS Optica IRAS 100

WENSS 92cm NVSS 20cm How did the universe form? There is a lot of it (petabytes) ROSAT ~keV GB 6cm Why Astronomy Data? There is lots of it High dimensional Spatial temporal Great sandbox for data mining algorithms

Can share cross company University researchers Great way to teach both Astronomy and Computational Science Want to federate many instruments Butsome science is hitting a wall FTP and GREP are not adequate You can GREP 1 MB in a second You can GREP 1 GB in a minute You can GREP 1 TB in 2 days You can GREP 1 PB in 3 years.

You can FTP 1 MB in 1 sec You can FTP 1 GB / min (= 1 $/G 2 days and 1K$ 3 years and 1M$ Oh!, and 1PB ~10,000 disks At some point you need indices to limit search parallel data search and analysis This is where databases can help SkyServer SkyServer.SDSS.org Like the TerraServer, but looking the other way: a picture of of the universe

Pixels + Data Mining Astronomers get about 400 attributes for each object Get Spectrograms for 1% of the objects Goal: Easy Data Publication & Access Augment FTP with data query: Return intelligent data subsets Make it easy to Publish: Record structured data Find: Find data anywhere in the network Get the subset you need Explore datasets interactively Realistic goal:

Make it as easy as publishing/reading web sites today. Web Services: The Key? Web SERVER: Given a url + parameters Returns a web page (often dynamic) Your h t program tp 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 b We e pag Your s oap program Data In your address space Web Server ect

j b o in l xm Web Service Data Federations of Web Services Massive datasets live near their owners: Near the instruments 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 ArchivesFederation A common global schema Grid and Web Services Synergy I believe the Grid will be many web services IETF standards Provide Naming Authorization / Security / Privacy Distributed Objects

Discovery, Definition, Invocation, Object Model Higher level services: workflow, transactions, DB,.. Synergy: commercial Internet & Grid tools SkyQuery (http://skyquery.net/) Distributed Query tool using a set of web services Feasibility study, built in 6 weeks from scratch 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 Structure Image cutout SkyNode First Web Page SkyNode 2Mass SkyQuery SkyNode SDSS Show Cutout Web Service

Outline The World Wide Telescope Idea Data Mining the Sloan Digital Sky Survey Spherical Geometry in SQL 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 The 20 Queries 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 -100.75. 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 60arc seconds. Q5: Find all galaxies with a deVaucouleours profile (r 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

in Ha >40 (Ha is the main hydrogen spectral line.) 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 600.1. 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

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

~30 lines/ spectrum 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(*) An easy one Q15: Provide a list of moving objects consistent with an asteroid. Sounds hard but there are 5 pictures of the object at 5 different times (colors) and so can compute velocity. Image pipeline computes velocity. Computing it from the 5 color x,y would also be fast Finds 285 objects in 3 minutes, 140MBps.

select objId, -- return object ID sqrt(power(rowv,2)+power(colv,2)) as velocity from photoObj -- check each object. where (power(rowv,2) + power(colv, 2)) -- square of velocity between 50 and 1000 -- huge values =error Q15: Fast Moving Objects Find near earth asteroids: SELECT r.objID as rId, g.objId as gId, r.run, r.camcol, r.field as field, g.field as gField, r.ra as ra_r, r.dec as dec_r, g.ra as ra_g, g.dec as dec_g, sqrt( power(r.cx -g.cx,2)+ power(r.cy-g.cy,2)+power(r.cz-g.cz,2) )*(10800/PI()) as distance FROM PhotoObj r, PhotoObj g WHERE r.run = g.run and r.camcol=g.camcol and abs(g.field-r.field)<2 -- the match criteria -- the red selection criteria

and ((power(r.q_r,2) + power(r.u_r,2)) > 0.111111 ) and r.fiberMag_r between 6 and 22 and r.fiberMag_r < r.fiberMag_g and r.fiberMag_r < r.fiberMag_i and r.parentID=0 and r.fiberMag_r < r.fiberMag_u and r.fiberMag_r < r.fiberMag_z and r.isoA_r/r.isoB_r > 1.5 and r.isoA_r>2.0 -- the green selection criteria and ((power(g.q_g,2) + power(g.u_g,2)) > 0.111111 ) and g.fiberMag_g between 6 and 22 and g.fiberMag_g < g.fiberMag_r and g.fiberMag_g < g.fiberMag_i and g.fiberMag_g < g.fiberMag_u and g.fiberMag_g < g.fiberMag_z and g.parentID=0 and g.isoA_g/g.isoB_g > 1.5 and g.isoA_g > 2.0 -- the matchup of the pair and sqrt(power(r.cx -g.cx,2)+ power(r.cy-g.cy,2)+power(r.cz-g.cz,2))*(10800/PI())< 4.0 and abs(r.fiberMag_r-g.fiberMag_g)< 2.0 Finds 3 objects in 11 minutes (or 27 seconds with an index) Ugly, but consider the alternatives (c programs an files and)

Q15: Fast Moving Objects Find near earth asteroids: SELECT r.objID as rId, g.objId as gId, dbo.fGetUrlEq(g.ra, g.dec) as url FROM PhotoObj r, PhotoObj g WHERE r.run = g.run and r.camcol=g.camcol and abs(g.field-r.field)<2 -- nearby -- the red selection criteria and ((power(r.q_r,2) + power(r.u_r,2)) > 0.111111 ) and r.fiberMag_r between 6 and 22 and r.fiberMag_r < r.fiberMag_g and r.fiberMag_r < r.fiberMag_i and r.parentID=0 and r.fiberMag_r < r.fiberMag_u and r.fiberMag_r < r.fiberMag_z and r.isoA_r/r.isoB_r > 1.5 and r.isoA_r>2.0 -- the green selection criteria and ((power(g.q_g,2) + power(g.u_g,2)) > 0.111111 ) and g.fiberMag_g between 6 and 22 and g.fiberMag_g < g.fiberMag_r and g.fiberMag_g < g.fiberMag_i and g.fiberMag_g < g.fiberMag_u and g.fiberMag_g < g.fiberMag_z

and g.parentID=0 and g.isoA_g/g.isoB_g > 1.5 and g.isoA_g > 2.0 -- the matchup of the pair and sqrt(power(r.cx -g.cx,2)+ power(r.cy-g.cy,2)+power(r.cz-g.cz,2))*(10800/PI())< 4.0 and abs(r.fiberMag_r-g.fiberMag_g)< 2.0 Finds 3 objects in 11 minutes (or 52 seconds with an index) Ugly, but consider the alternatives (c programs and files and time) Performance (on current SDSS data) (10 mips/IO, 200 ins/byte) 2.5 m rec/s/cpu seconds 1000

10 1 cpu vs IO ~1,000 IO/cpu sec 1,000 IOs/cpu ~ sec 64 MB IO/cpu sec 1E+3 1E+2 1E+1 time vs queryID 1E+0 cpu elapsed 100

IOcount Run times: on 15k$ HP Server (2 cpu, 1 GB , 8 disk) Some take 10 minutes 1E+7 Some take 1 minute 1E+6 Median ~ 22 sec. 1E+5 Ghz processors are fast! 1E+4 0.01 0.1 1. CPU sec 10. 100.

1,0 ae Q08 Q01 Q09 Q10A Q19 Q12 Q10 Q20

Q16 Q02 Q13 Q04 Q06 Q11 Q15B Q17 Q07 Q14

Q15A Q05 Q03 Q18 Outline The World Wide Telescope Idea Data Mining the Sloan Digital Sky Survey Spherical Geometry in SQL Spherical Geometry Astronomy has redshifts (3D), but often works with celestial sphere (2D) Distance ~ arc angle Extended SQL to have neighbor functions GetNearestObject() returns a table with one row

GetNearbyObjects() returns a table Hierarchical Triangular Mesh (HTM) Szalay, Kunszt, Brunner http://www.sdss.jhu.edu/htm Every object has a 20-deep Mesh ID Given an area routine returns set of 2,0 covering triangles 2,1 Each triangle implies 2,2 2,3 range query: 2,3,0 2,3,1 2,3,2

2,3,3 htmID in triangle iff htmID in [traingle.mintriangle.max) 2 Reject false positives with careful geometry test Very fast: 10,000 triangles / second / cpu Using Hierarchical Triangular Mesh select * from photoObj as p, fHtmCover(x,y,z,r) as n where p.htmID between n.start and n.end and (2*asin(sqrt(power(x-cx,2)+power(y-cy,2)+power(z-cz,2))/2)) < radians(r) This is packaged as:

This is the fGetNearbyObjects(x,y,z,r) geometry test Spherical Areas SphericalArea = { ConvexArea} ConvexArea = { SphericalEdge} = {PlaneSphereIntersect} Plane = normal unit vector v (vx,vy,vz) length l. Point p= (x,y,z) on the unit sphere is inside the edge if (xyz)(vx,vy,vz)> l. A point is inside a convex area if it is inside each of the edges. Non-convex areas are convex area unions. Swiss-cheese areas (holes in them)

are positive and negative convex areas actually, just negative lengths. l -convex3 +convex2 +convex1 Areas as Tables --- An area is a set of convexes that have a set of edges. create table Area ( AreaID integer, -- the unique identifier of the area ConvexID integer, -- the unique identifier of a convex EdgeID integer, -- unique id of the edge of an edge

x float, -- the xyz vector of the edge (v) y float, -z float, -l float, -- the vector length. primary key (AreaID, ConvexID, EdgeID) ) Point in Convex declare @aID int -- area ID is a parameter select * -- return all points from Points p -where not exists ( -- where there is no outside edge.

select EdgeID -- for all edges from Area a -where AreaID = @aID -- in the area and (p. x*a.x + p. y*a.y + p. z*a.z) < a.l) - test outside ) Point in Polygon (union of convexes) select * -- return all points from Points p -- in the area where exists ( -- Where there is a convex select ConvexID -- there is a convex from area a -- in the area

where AreaID = @aID -- in the area and no points and (p. x*a.x + p. y*a.y + p. z*a.z) < a.l -group by all ConvexID -- outside an edge of the convex having count(*) = 0 -- (no outside points) ) A Simple Extension Do this for the plane and for N-space (rather than the sphere) Thats easy, so . A harder problem that took me 2 years, So, you should get it in an hour A harder problem: compute the materialized view: (object, neighborObject, distance) for all distance less than 30 asec Using nearby function: 1 cpu day/ 1 Mobj Using set operators: 1 cpu day/ 100 Mobj

An interesting algorithm: hint: break into horizontal zones 30asec high Join 3 pairs of zones. Worry about wrap-around on the sphere So what? SQL is a functional programming language, perhaps the most popular one. Set problems are easy in set-oriented languages. We (I) have not been thinking in sets (since I left the math department). This set-oriented approach is faster than the HTM function because it is inside the DB and it is batched Summary

The World Wide Telescope Idea Data Mining the Sloan Digital Sky Survey Spherical Geometry in SQL Call to Action If you do data visualization: we need you (and we know it). If you do databases: here is some data you can practice on. If you do distributed systems: here is a federation you can practice on. If you do data mining here is a dataset to test your algorithms. If you do astronomy educational outreach here is a tool for you. SkyServer 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 Surveys (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 SkyServerPublic 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.

The archive will enable astronomers to explore the data interactively. Data access will be aided by multidimensional spatial and attribute indices. The data will be partitioned in many ways. Small tag objects consisting of the most popular attributes will accelerate frequent searches. Splitting the data among multiple servers will allow parallel, scalable I/O and parallel data analysis. Hashing techniques will allow efficient clustering, and pair-wise comparison algorithms that should parallelize nicely. Randomly sampled subsets will allow de-bugging otherwise large queries at the desktop. Central servers will operate a data pump to support sweep searches touching most of the data. The anticipated queries will re-quire special operators related to angular distances and complex similarity tests of object properties, like shapes, colors, velocity vectors, or temporal behaviors. These issues pose interesting data management challenges.

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