Designing and mining multi-terabyte astronomy archives: the Sloan Digital Sky Survey

  • Authors:

  • Alexander S. Szalay;Peter Z. Kunszt;Ani Thakar;Jim Gray;Don Slutz;Robert J. Brunner

  • Affiliations:

  • Dept. of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD;Dept. of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD;Dept. of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD;Microsoft Research, San Francisco, CA;Microsoft Research, San Francisco, CA;California Institute of Technology, Pasadena, CA

  • Venue:
  • SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
  • Year:
  • 2000

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Abstract

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 require 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.