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Analysing Web Traffic

A Case Study on Artificial and Genuine Advertisement-Related Behaviour

Mariusz Kozakiewicz, Agnieszka Jastrzębska, Marek Gajewski, et al.

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ca. 160,49

Springer Nature Switzerland img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Allgemeines, Lexika

Beschreibung

This book presents ample, richly illustrated account on results and experience from a project, dealing with the analysis of data concerning behavior patterns on the Web. The advertising on the Web is dealt with, and the ultimate issue is to assess the share of the artificial, automated activity (ads fraud), as opposed to the genuine human activity.

After a comprehensive introductory part, a full-fledged report is provided from a wide range of analytic and design efforts, oriented at: the representation of the Web behavior patterns, formation and selection of telling variables, structuring of the populations of behavior patterns, including the use of clustering, classification of these patterns, and devising most effective and efficient techniques to separate the artificial from the genuine traffic.

A series of important and useful conclusions is drawn, concerning both the nature of the observed phenomenon, and hence the characteristics of the respective datasets, and theappropriateness of the methodological approaches tried out and devised. Some of these observations and conclusions, both related to data and to methods employed, provide a new insight and are sometimes surprising.

The book provides also a rich bibliography on the main problem approached and on the various methodologies tried out.

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Schlagwörter

Clustering Analysis, Web Traffic, Computational Intelligence, Data Science, Data Acquisition, Big Data