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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Data-Stream Sampling: Basic Techniques and Results
verfasst von : Peter J. Haas
Erschienen in: Data Stream Management
Verlag: Springer Berlin Heidelberg
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Perhaps the most basic synopsis of a data stream is a sample of elements from the stream. A key benefit of such a sample is its flexibility: the sample can serve as input to a wide variety of analytical procedures and can be reduced further to provide many additional data synopses. If, in particular, the sample is collected using random sampling techniques, then the sample can form a basis for statistical inference about the contents of the stream. This chapter surveys some basic sampling and inference techniques for data streams. We focus on general methods for materializing a sample; later chapters provide specialized sampling methods for specific analytic tasks.