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Feature-preserved Sampling Over Streaming Data by Kun-Ta Chuang; Hung-Leng Chen; Ming-Syan Chen is a Computer Science article available to read on EtoBox.

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In this article, we explore a novel sampling model, called__feature preserved sampling__(__FPS__) that sequentially generates a high-quality sample over sliding windows. The sampling quality we consider refers to the degree of consistency between the sample proportion and the population proportion of each attribute value in a window. Due to the time-variant nature of real-world datasets, users are more likely to be interested in the most recent data. However, previous works have not been able to generate a high-quality sample over sliding windows that precisely preserves up-to-date population characteristics. Motivated by this shortcoming, we have developed the__FPS__algorithm, which has several advantages: (1) it sequentially generates a sample from a time-variant data source over sliding windows; (2) the execution time of__FPS__is linear with respect to the database size; (3) the__relative__proportional differences between the sample proportions and population proportions of most distinct attribute values are guaranteed to be below a specified error threshold, ε, while the__relative__proportion differences of the remaining attribute values are as close to ε as possible, which ens

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Author

Kun-Ta Chuang; Hung-Leng Chen; Ming-Syan Chen

Publisher

ACM

Published

2009

Language

EN

Field

Computer Science (Physical Sciences)