Titlebook: Data Stream Management; Processing High-Spee Minos Garofalakis,Johannes Gehrke,Rajeev Rastogi Textbook 2016 Springer-Verlag Berlin Heidelbe

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SLING

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Clustering Data Streamsocus on clustering in a streaming scenario where a small number of data items are presented at a time and we cannot store all the data points. Thus, our algorithms are restricted to a single pass. The space restriction is typically sublinear, ., where the number of input points is ..

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Outmoded

Ron Elber,Benoit Roux,Roberto Olenderm. 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.

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https://doi.org/10.1007/978-3-319-60919-5other application is to detecting network anomalies by monitoring network traffic. We describe a variety of approaches that have been proposed to solve these problems. Our goal is to give a flavor of the various techniques that have been used in this area.

Kaleidoscope

Multiscale Computational Materials Science data from the data stream to make each decision required by the learning process. The method is applicable to essentially any induction algorithm based on discrete search. In this chapter, we illustrate the use of our method by applying it to what is perhaps the most widely used form of data mining: decision tree induction.

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令人不快

Data-Stream Sampling: Basic Techniques and Resultsm. 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.