Among the most important decisions to be taken in modeling human habits in smart spaces there is the choice of the technique to be adopted: models can be expressed by using a multitude of formalisms, all with differently proven effectiveness. However, a crucial aspect, often underestimated in its importance, is the readability of the model: it influences the possibility of validating the model itself by human experts.
Data interoperability refers to the issue of accessing and processing data from multiple sources in order to create more holistic and contextual information for improving data analysis, for better decision-making, and for accountability purposes. In the era towards a data-driven society, the notion of data interoperability is of paramount importance. Looking at the research work in the last decades, several types of data interoperability scenarios emerged, including the following.
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