Marco Carnuccio

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Probabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main (and perhaps strong) assumption of these models is that generative process follows a bag-of-words assumption, i.e. each token is independent from the previous one. We extend the popular Latent Dirichlet Allocation model by(More)
The Web is an evolving system, which tries to adapt to the needs of users. The transition to Web2.0, and, currently, to Web3.0, are the expression of this trend: the goal is to focus on the leading role of the end user in Web browsing, which should be supported by adequate tools. In this paper, we propose <i>Bor&#232;</i>, an architectural paradigm for(More)
This paper presents an approach to the discovery of predictive process models, which combines a series of data mining techniques (ranging from pattern mining, to non-parametric regression and to predictive clustering) with ad-hoc data transformation and abstraction mechanisms. As a result, a modular representation of the process is obtained, where different(More)
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