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Evaluating Knowledge Anchors in Data Graphs Against Basic Level Objects Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Big Data Analysis Framework for Model-Based Web User Behavior Analytics

Authors : Carlo Bernaschina, Marco Brambilla, Andrea Mauri, Eric Umuhoza

Published in: Web Engineering

Publisher: Springer International Publishing

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Abstract

While basic Web analytics tools are widespread and provide statistics about website navigation, no approaches exist for merging such statistics with information about the Web application structure, content and semantics. Current analytics tools only analyze the user interaction at page level in terms of page views, entry and landing page, page views per visit, and so on. We show the advantages of combining Web application models with runtime navigation logs, at the purpose of deepening the understanding of users behaviour. We propose a model-driven approach that combines user interaction modeling (based on the IFML standard), full code generation of the designed application, user tracking at runtime through logging of runtime component execution and user activities, integration with page content details, generation of integrated schema-less data streams, and application of large-scale analytics and visualization tools for big data, by applying both traditional data visualization techniques and direct representation of statistics on visual models of the Web application.

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Metadata
Title
A Big Data Analysis Framework for Model-Based Web User Behavior Analytics
Authors
Carlo Bernaschina
Marco Brambilla
Andrea Mauri
Eric Umuhoza
Copyright Year
2017
Book
Web Engineering

Print ISBN: 978-3-319-60130-4

Electronic ISBN: 978-3-319-60131-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-60131-1_6

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