Linked Bernoulli Synopses: Sampling along Foreign Keys

Random sampling is a popular technique for providing fast approximate query answers, especially in data warehouse environments. Compared to other types of synopses, random sampling bears the advantage of retaining the dataset’s dimensionality; it also associates probabilistic error bounds with the query results. Most of the available sampling techniques focus on table-level sampling, that is, they produce a sample of only a single database table. Queries that contain joins over multiple tables cannot be answered with such samples because join results on random samples are often small and skewed. On the contrary, schema-level sampling techniques by design support queries containing joins. In this paper, we introduce Linked Bernoulli Synopses, a schemalevel sampling scheme based upon the well-known Join Synopses. Both schemes rely on the idea of maintaining foreign-key integrity in the synopses; they are therefore suited to process queries containing arbitrary foreign-key joins. In con...

• Type: Conference
• Year: 2008
• Where: SSDBM
• Authors: Rainer Gemulla, Philipp Rösch, Wolfgang Lehner
• Keywords: Bernoulli Synopses, Database, Random Sampling, Sampling Techniques, SSDBM 2008