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

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?

Driving Style and Behavior Analysis based on Trip Segmentation over GPS Information through Unsupervised Learning

Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage “better” driving behaviour through immediate feedback while driving, or by scaling auto insurance … Continue reading Driving Style and Behavior Analysis based on Trip Segmentation over GPS Information through Unsupervised Learning

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