Abstract

Current and future astronomical surveys are producing catalogs with millions and billions of objects. On-line access to such big datasets for data mining and cross-correlation is usually as highly desired as unfeasible. Providing these capabilities is becoming critical for the Virtual Observatory framework. In this paper we present various performance tests that show how using Relational Database Management Systems (RDBMS) and a Zoning algorithm to partition and parallelize the computation, we can facilitate large-scale query and cross-match.