Big Data technology has discarded traditional data modelling approaches as no longer applicable to distributed data processing. It is, however, largely recognised that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g. data analytics and reporting. In this paper, the third of its kind co-authored by mem-

In the last few years the security of the user's identity has become of paramount importance. In this paper we investigate the opportunity of using a multimodal biometric system as input of a fuzzy controller designed with the aim of preventing user substitution after the initial authentication process.

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Big Data' techniques are often adopted in crossorganization scenarios for integrating multiple data sources to extract statistics or other latent information. Even if these techniques do not require the support of a schema for processing data, a common conceptual model is typically defined to address name resolution. This implies that each local source is tasked of applying a semantic lifting procedure for expressing the local data in term of the common model. Semantic heterogeneity is then potentially introduced in data. In this paper we illustrate a methodology designed to the implementation of consistent process mining algorithms in a 'Big Data' context. In particular, we exploit two different procedures. The first one is aimed at computing the mismatch among the data sources to be integrated. The second uses mismatch values to extend data to be processed with a traditional map reduce algorithm.