While advancements in big data analytics have done a good job at helping marketers target mass markets and people of like interests, they fall short of understanding a person's unique interests and going that extra mile of treating people like individuals. While there is no argument that people have overlapping interests, there is a false assumption that just because a person falls into a certain category (i.e. A person will express hundreds of different interests that extend beyond any given category; this is what makes a person unique. Instead, they tend to bucket people into broad categories. Big data needs many data points to evaluate whether an interest is statistically significant.

Over the analyses being increasingly diversified, the notion of referential becomes more and more important in the theoretical plan and in the associated descriptions. We introduce the specification of this notion of referential with several examples, and then we propose its formalized approach which would admit the computing treatments later. We put this notion within the theoretical framework of the temporality (i.e. aspecto-temporal theory) apprehended by the languages while arguing its introduction and formalizing their concepts.