Spatial Searches in the ODM. slide 2 Common Spatial Questions Points in region queries 1.Find all objects in this region 2.Find all “good” objects (not.

Slides:



Advertisements
Similar presentations
Spatial (or N-Dimensional) Search in a Relational World Jim Gray, Microsoft Alex Szalay, Johns Hopkins U.
Advertisements

Spatial (or N-Dimensional) Search in a Relational World Jim Gray.
1 There Goes the Neighborhood! Spatial (or N-Dimensional) Search in a Relational World Jim Gray, Microsoft Alex Szalay, Johns Hopkins U.
Registries Work Package 2 Requirements, Science Cases, Use Cases, Test Cases Charter: Focus on science case scenarios, and use cases related specifically.
Data Mining, ADQL, & The National Virtual Observatory's OpenSkyQuery Utility by Richard Doc Kinne, KQR 2008 AAVSO Fall Conference Nantucket, MA.
9 September 2005NVO Summer School Aspen Astronomical Dataset Query Language (ADQL) Ray Plante T HE US N ATIONAL V IRTUAL O BSERVATORY.
Footprint Service Specification NVO Summer School 2008 Gretchen Greene (thanks to Tamas Budavari and Francois Bonnarel) T HE US N ATIONAL V IRTUAL O BSERVATORY.
Tony Rees Divisional Data Centre CSIRO Marine Research, Australia Application of c-squares spatial indexing to an archive of remotely.
COMP 175 | COMPUTER GRAPHICS Remco Chang1/6103b – Shapes Lecture 03b: Shapes COMP 175: Computer Graphics February 3, 2015.
The Relational Model and Relational Algebra Nothing is so practical as a good theory Kurt Lewin, 1945.
Copyright © 2004 Pearson Education, Inc.. Chapter 15 Algorithms for Query Processing and Optimization.
Approximations of points and polygonal chains
CSE 681 Bounding Volumes. CSE 681 Bounding Volumes Use simple volume enclose object(s) tradeoff for rays where there is extra intersection test for object.
Searching on Multi-Dimensional Data
Extended Gaussian Images
1 Enviromatics Spatial database systems Spatial database systems Вонр. проф. д-р Александар Маркоски Технички факултет – Битола 2008 год.
Introduction to Spatial Database System Presented by Xiaozhi Yu.
3D Shape Histograms for Similarity Search and Classification in Spatial Databases. Mihael Ankerst,Gabi Kastenmuller, Hans-Peter-Kriegel,Thomas Seidl Univ.
Polygons and the convex hull Prof. Noah Snavely CS1114
László Dobos 1,2, Tamás Budavári 2, Nolan Li 2, Alex Szalay 2, István Csabai 1 1 Eötvös Loránd University, Budapest,
Query Processing in Databases Dr. M. Gavrilova.  Introduction  I/O algorithms for large databases  Complex geometric operations in graphical querying.
Web + VO + Database Technologies = HLA Footprints STScI: Gretchen Greene, Steve Lubow, Brian McLean, Rick White and the HLA Team JHU: Alex Szalay and Tamas.
Vertices and Fragments I CS4395: Computer Graphics 1 Mohan Sridharan Based on slides created by Edward Angel.
20 Spatial Queries for an Astronomer's Bench (mark) María Nieto-Santisteban 1 Tobias Scholl 2 Alexander Szalay 1 Alfons Kemper 2 1. The Johns Hopkins University,
CSE351/ IT351 Modeling And Simulation Choosing a Mesh Model Dr. Jim Holten.
Spatio-Temporal Databases. Introduction Spatiotemporal Databases: manage spatial data whose geometry changes over time Geometry: position and/or extent.
Spatial Indexing I Point Access Methods. Spatial Indexing Point Access Methods (PAMs) vs Spatial Access Methods (SAMs) PAM: index only point data Hierarchical.
Spatial Indexing I Point Access Methods.
Chap8: Trends in DBMS 8.1 Database support for Field Entities 8.2 Content-based retrieval 8.3 Introduction to spatial data warehouses 8.4 Summary.
C&A 10April06 1 Point Source Detection and Localization Using the UW HealPixel database Toby Burnett University of Washington.
SDSS Web Services Tamás Budavári Johns Hopkins University Coding against the Universe.
László Dobos, Tamás Budavári, Alex Szalay, István Csabai Eötvös University / JHU Aug , 2008.IDIES Inaugural Symposium, Baltimore1.
PS1 PSPS Object Data Manager Design PSPS Critical Design Review November 5-6, 2007 IfA.
How to speed up search of ILMT light curves using the HTM (Hierarchical Triangular Mesh) method in relational databases ARC Liège, 11 February 2010 ILMT.
Context Tailoring the DBMS –To support particular applications Beyond alphanumerical data Beyond retrieve + process –To support particular hardware New.
A Metadata Based Approach For Supporting Subsetting Queries Over Parallel HDF5 Datasets Vignesh Santhanagopalan Graduate Student Department Of CSE.
Access Path Selection in a Relational Database Management System Selinger et al.
6. Simple Features Specification Background information UML overview Simple features geometry.
2D/3D Shape Manipulation, 3D Printing Shape Representations Slides from Olga Sorkine February 20, 2013 CS 6501.
Database System Concepts ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com 1 Indexing Spatial Data.
Greg Janée chit-chat with CS database folks 10/26/01 Gazetteer database 4.5 million items, each having: –1+ names fair to good discriminator –1 geospatial.
Footprint Service Specification IVOA Interop Meeting Trieste 2008 Gretchen Greene and Tamas Budavari.
P Structured Query Language for Virtual Observatory Yuji Shirasaki National Astronomical Observatory of Japan, and Masahiro Tanaka (NAOJ), Satoshi.
2003 Apr 81 Indexing the Sky Clive Page Apr 82.
G. Fekete, JHU Efficient search indices for geospatial data in a relational database Gyorgy (George) Fekete Dept. Physics and Astronomy Johns Hopkins University.
Data Types Entities and fields can be transformed to the other type Vectors compared to rasters.
9/28/2007IVOA Interop1 Implementing the Region Syntax A.Szalay, T.Budavari, P.Dowler, +ADQL Working Group.
Spatial and Geographic Databases ADVANCED DATABASES Khawaja Mohiuddin Assistant Professor Department of Computer Sciences Bahria University (Karachi Campus)
Spatial Database 2/5/2011 Reference – Ramakrishna Gerhke and Silbershatz.
Spatial and Geographic Databases. Spatial databases store information related to spatial locations, and support efficient storage, indexing and querying.
Recent spatial work by Jim Gray and Alex Szalay Bob Mann.
Spatial Indexing Techniques Introduction to Spatial Computing CSE 5ISC Some slides adapted from Spatial Databases: A Tour by Shashi Shekhar Prentice Hall.
January 23, 2016María Nieto-Santisteban – AISRP 2003 / Pittsburgh1 High-Speed Access for an NVO Data Grid Node María A. Nieto-Santisteban, Aniruddha R.
Spatio-Temporal Databases
Lecture 3 With every passing hour our solar system comes forty-three thousand miles closer to globular cluster 13 in the constellation Hercules, and still.
1 Giuseppe Romeo Voronoi based Source Detection. 2 Voronoi cell The Voronoi tessellation is constructed as follows: for each data point  i (also called.
Content  Hierarchical Triangle Mesh (HTM)  Perrizo Triangle Mesh Tree (PTM-tree)  SDSS.
Slide 1 PS1 PSPS Object Data Manager Design PSPS Critical Design Review November 5-6, 2007 IfA.
Catalogs contain hundreds of millions of objects
Astronomy Application: (National Virtual Observatory data)
Standard Query Language for VO
Cross-matching the sky with database server cluster
Sky Query: A distributed query engine for astronomy
Query Processing in Databases Dr. M. Gavrilova
File Systems and Databases
Algorithm design (computational geometry)
TAP Standards and Feedback
Efficient Catalog Matching with Dropout Detection
Footprint Service Specification
Presentation transcript:

Spatial Searches in the ODM

slide 2 Common Spatial Questions Points in region queries 1.Find all objects in this region 2.Find all “good” objects (not in masked areas) 3.Is this point in any of the regions Region in region 4.Find regions near this region and their area 5.Find all objects with error boxes intersecting region 6.What is the common part of these regions Various statistical operations 7.Find the object counts over a given region list 8.Cross-match these two catalogs in the region

slide 3 Sky Coordinates of Points  Many different coordinate systems Equatorial, Galactic, Ecliptic, Supergalactic  Longitude-latitude constraints  Searches often in mix of different coordinate systems gb>40 and dec between 10 and 20 Problem: coordinate singularities, transformations  How can one describe constraints in a easy, uniform fashion?  How can one perform fast database queries in an easy fashion? Fast:Indexes Easy: simple query expressions

slide 4 Describing Regions Spacetime metadata for the VO (Arnold Rots)  Includes definitions of Constraint: single small or great circle Convex: intersection of constraints Region: union of convexes  Support both angles and Cartesian descriptions  Constructors for CIRCLE, RECTANGLE, POLYGON, CONVEX HULL  Boolean algebra (INTERSECTION, UNION, DIFF)  Proper language to describe the abstract regions  Similar to GIS, but much better suited for astronomy

slide 5 Things Can Get Complex

slide 6 We Do Spatial 3 Ways  Hierarchical Triangular Mesh (extension to SQL) Uses table valued functions Acts as a new “spatial access method”  Zones: fits SQL well Surprisingly simple & good  3D Constraints: a novel idea Algebra on regions, can be implemented in pure SQL

slide 7 PS1 Footprint  Using the projection cell definitions as centers for tessellation (T. Budavari)

slide 8 CrossMatch: Zone Approach  Divide space into declination zones  Objects ordered by zoneid, ra (on the sphere need wrap-around margin.)  Point search look in neighboring zones within ~ (ra ± Δ) bounding box  All inside the relational engine  Avoids “impedance mismatch”  Can “batch” comparisons  Automatically parallel  Details in Maria’s thesis r ra-zoneMax zoneMax x ra ± Δ

slide 9 Indexing Using Quadtrees  Cover the sky with hierarchical pixels  COBE – start with a cube  Hierarchical Triangular Mesh (HTM) uses trixels Samet, Fekete  Start with an octahedron, and split each triangle into 4 children, down to 20 levels deep  Smallest triangles are 0.3”  Each trixel has a unique htmID 2,2 2,1 2,0 2,3 2,3,0 2,3,1 2,3,22,3,

slide 10 Space-Filling Curve ,2, [0.12,0.13) [0.122,0.123)[0.121,0.122)[0.120,0.121)[0.123,0.130) Triangles correspond to ranges All points inside the triangle are inside the range. [0.122,0.130) [0.120,0.121)

slide 11 SQL HTM Extension  Every object has a 20-deep htmID (44bits)  Clustered index on htmID  Table-valued functions for spatial joins Given a region definition, routine returns up to 10 ranges of covering triangles Spatial query is mapped to ~10 range queries  Current implementation rewritten in C#  Excellent performance, little calling overhead  Three layers General geometry library HTM kernel IO (parsing + SQL interface)

slide 12 Writing Spatial SQL -- region description is contained TABLE (htmStart bigint,htmEnd bigint) SELECT * from -- TABLE ( convexId bigint,x float, y float, z float) SELECT -- SELECTo.ra, o.dec, 1 as flag, o.objid FROM (SELECT objID as objid, cx,cy,cz,ra,[dec] FROM Objects q AS c ON q.htmID between c.HtmIdStart and c.HtmIdEnd ) AS o WHERE NOT EXISTS ( SELECT p.convexId AS p WHERE (o.cx*p.x + o.cy*p.y + o.cz*p.z < p.c) GROUP BY p.convexId )

slide 13 Status  All three libraries extensively tested  Zones used for Maria’s thesis, plus various papers  New HTM code in production use since July on SDSS  Same code also used by STScI HLA, Galex  Systematic regression tests developed  Footprints computed for all major surveys  Complex mask computations done on SDSS  Loading: zones used for bulk crossmatch  Ad hoc queries: use HTM-based search functions  Excellent performance