Effective Computation of Biased Quantiles over Data Streams

Skewis prevalentin manydata sourcessuchas IP traffic streams. To continually summarize the distribution of such data, a highbiased set of quantiles (e.g., 50th, 90th and 99th percentiles) with finer error guarantees at higher ranks (e.g., errors of 5, 1 and 0.1 percent, respectively)is moreuseful than uniformly distributed quantiles (e.g., 25th, 50th and 75th percentiles) with uniform error guarantees. In this paper, we address the following two problems. First, can we compute quantiles with finer error guarantees for the higher ranks of the data distribution effectively, using less space and computation time than computing all quantiles uniformly at the finest error? Second, if specific quantiles and their errorboundsarerequesteda priori, can the necessaryspaceusage and computation time be reduced? We answerboth questionsin the affirmativebyformalizing them as the "high-biased"quantiles and the "targeted"quantiles problems, respectively, and presenting algorithms ...

• Type: Conference
• Year: 2005
• Where: ICDE
• Authors: Graham Cormode, Flip Korn, S. Muthukrishnan, Divesh Srivastava
• Keywords: Database, Finer Error Guarantees, ICDE 2005, Specific Quantiles, Uniform Error Guarantees