Exploratory computing: a draft manifesto (DSAA)

The advent of the Big Data challenge has stimulated research on methods and techniques to deal with the problem of managing data abundance. Many approaches have been developed, but for the most part, they attack one specific side of the problem: e.g. efficient querying, analysis techniques that summarize data or reduce its dimensionality, data visualization, etc. The approach proposed in this paper aims instead at taking a comprehensive view: first of all, it takes into account that human exploration is an iterative and multi-step process and therefore allows building upon a previous query on to the next, in a sort of “dialogue” between the user and the system. Second, it aims at supporting a variety of user experiences, like investigation, inspiration seeking, monitoring, comparison, decision-making, research, etc. Third, and probably most important, it adds to the notion of “big” the notion of “rich”: Exploratory Computing (EC) aims at dealing with datasets of semantically complex items, whose inspection may reach beyond the user's previous knowledge or expectations: an exploratory experience basically consists in creating, refining, modifying, comparing various datasets in order to “make sense” of these meanings.