Human Centric Data Management

With “human centric data management”, we denote all kind of practical and theoretical developments that contribute to the improvement of data management for human users, such that it becomes better to understand, easier to handle, and more natural to communicate with. The popularity of digital applications, social media and multimedia created a shift towards “big data” that are characterized by huge data volumes, a large variety of data formats, fast data processing requirements, and veracity problems. The more data we have at our disposal, the more applications arise, but also the more sophisticated these applications become. Along with these technological developments comes the awareness that there is a growing need for human centric data management tools. Indeed, perfect data sets are rare and data imperfections propagate to imperfect data processing solutions. Humans communicate in natural language and cope with imperfect information in their everyday behavior, whereas conventional data management assumes that data are perfect and data manipulation is based on a bivalent Boolean logic. Computational intelligence techniques, more specifically soft computing and fuzzy set theory, offer the tools for bridging the gap between the way humans behave and communicate and the way conventional data management tools work. This is especially the case because they allow to generalize bivalent Boolean logic into multivalued fuzzy logic and offer sound foundations for uncertainty modeling that are less stringent, but broader applicable, than conventional probability theory. This special issue is an initiative of the working group on “Soft Computing in Database Management and Information Retrieval” of the European Society for