Sampling from databases using B+-trees
Dimuthu Makawita, Kian‐Lee Tan, Huan Liu · 2000
Sampling techniques are becoming increasingly important for large databases.How ever, the problem of obtaining a random sample from index structures has not received muc h atten tion.In this paper, we examine sampling techniques for B + -tree.As the fanout of each n o d e v aries, a random walk through the index structure does not produce a good represen tativ e sample of the data set.We propose a new technique, called B + -T ree 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 BTWRS outperforms existing schemes in terms of the quality of the samples obtained and the eÆciency of the sampling process.The proposed method can be readily adopted in existing commercial systems.