Marco Brambilla

a49a9-ifml-logoI’m associate professor of Web Science and Software Engineering at Politecnico di Milano, Italy.

I lead the Data Science Lab at Politecnico di Milano, DEIB.

My current research interests are on Web Science, Big Data Analysis,  Social Media Analytics, and Model-driven Development.

I’m the inventor of the Interaction Flow Modeling Language (IFML) standard by the OMG, and of 2 patents on crowdsourcing and multi-domain search.

I started-up Fluxedo and WebRatio.

I currently teach:

  • Web Science (see course materials here)
  • Software Engineering
  • Advanced Software Engineering (Model-driven Engineering, see book here)

My most recent books:

 9781627057080-MDSE-Book-Brambilla-Cabot-Wimmer-modeling-small


Model Driven Engineering in Practice

(second edition)

Web Information Retrieval

Model-Driven UI Engineering
of Web and Mobile Apps with IFML

Recent Posts

Understanding Polarized Political Events through Social Media Analysis

Predicting the outcome of elections is a topic that has been extensively studied in political polls, which have generally provided reliable predictions by means of statistical models. In recent years, online social media platforms have become a potential alternative to traditional polls, since they provide large amounts of post and user data, also referring to … Continue reading Understanding Polarized Political Events through Social Media Analysis

Data Cleaning for Knowledge Extraction and Understanding on Social Media

  Social media platforms let users share their opinions through textual or multimedia content. In many settings, this becomes a valuable source of knowledge that can be exploited for specific business objectives. Brands and companies often ask to monitor social media as sources for understanding the stance, opinion, and sentiment of their customers, audience and … Continue reading Data Cleaning for Knowledge Extraction and Understanding on Social Media

Iterative knowledge extraction from social networks

Our motivation starts from the fact that knowledge in the world continuously evolves, and thus ontologies and knowledge bases are largely incomplete. We explored iterative methods, using the results as new seeds. In this paper we address the following research questions:

How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds?
How does the reconstructed domain knowledge spread geographically?
Can the method be used to inspect the past, present, and future of knowledge?
Can the method be used to find emerging knowledge?

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