Affiliations: [a] School of ICT, NgeeAnn Polytechnic, 535 Clementi Road, Singapore 599489. Tel.: +65 460 6897; E-mail: [email protected] | [b] Department of Computer Science, National University of Singapore, 3 Science Drive 2, Singapore 117543. Tel.: +65 874 2862; E-mail: [email protected] | [c] Department of Computer Science & Engineering, Arizona State University, P.O. BOX 875406, Tempe, AZ 85287-5406, USA. Tel.: +1 480 727 7349; E-mail: [email protected]
Abstract: Sampling techniques are becoming increasingly important for very large databases. However, the problem of obtaining a random sample from index structures has not received much attention. In this paper, we examine sampling techniques for B+-tree. As the fanout of each node varies, a random walk through the index structure does not produce a good representative sample of the data set. We propose a new technique, called B+-Tree based Weighted Random Sampling (BTWRS), that alters the inclusion probabilities of records accordingly to allow more records from leaves, along the paths with higher fanouts, to be extracted. We extensively evaluated our method, and the results show that there is an improvement in BTWRS over the existing schemes in terms of the quality of the samples obtained and the efficiency of the sampling process. The proposed method can be readily adopted in existing commercial systems.
Keywords: B+-Tree, weighted random sampling, quality of samples
