Data Science Group – Politecnico di Milano

Integrating Modeling Languages and Web Logs for Enhanced User Behavior Analytics

While basic Web analytics tools are widespread and provide statistics about Web site navigation, no approaches exist for merging such statistics with information about the Web application structure, content and semantics.

We demonstrate 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.

A short video of the tool usage is available on YouTube.

The paper describing the approach is available in the WWW 2017 proceedings.

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