Skew is prevalent in data streams, and should be taken into account by algorithms that analyze the data. The problem of finding "biased quantiles"-- that is, approximate...
Graham Cormode, Flip Korn, S. Muthukrishnan, Dives...
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 perc...
Graham Cormode, Flip Korn, S. Muthukrishnan, Dives...
We present deterministic sub-linear space algorithms for a number of problems over update data streams, including, estimating frequencies of items and ranges, finding approximate ...
Much real data consists of more than one dimension, such as financial transactions (eg, price × volume) and IP network flows (eg, duration × numBytes), and capture relationship...
Graham Cormode, Flip Korn, S. Muthukrishnan, Dives...
We present lower bounds on the space required to estimate the quantiles of a stream of numerical values. Quantile estimation is perhaps the most studied problem in the data stream ...