Conceptual Modeling

Data Science is the study of the generalizable extraction of knowledge from data. A common epistemic requirement in assessing whether new knowledge is actionable for decision making is its predictive power, not just its ability to explain the past. The heterogeneity and scale of data and diversity of analytical methods require data scientists to have an integrated skill set, as well as a deep understanding of the craft of problem formulation and the science required to engineer effective solutions. I shall talk about the key issues that arise in industrial strength predictive modeling, including the implications for education in this fast emerging field. A Semiotic Approach to Conceptual Modelling Antonio L. Furtado, Marco A. Casanova, and Simone D.J. Barbosa Departmento de Informática Pontif́ıcia Universidade Católica do Rio de Janeiro (PUC-Rio) Rio de Janeiro, Brazil {furtado,casanova,simone}@inf.puc-rio.br Abstract. The work on Conceptual Modelling performed by our group The work on Conceptual Modelling performed by our group at PUC-Rio is surveyed, covering four mutually dependent research topics. Regarding databases as a component of information systems, we extended the scope of the Entity-Relationship model, so as to encompass facts, events and agents in a three-schemata specification method employing a logic programming formalism. Next we proceeded to render the specifications executable, by utilizing backward-chaining planners to satisfy the agents’ goals through sequences of fact-modification events. Thanks to the adoption of this plan-recognition / plan-generation paradigm, it became possible to treat both business-oriented and fictional narrative genres. To guide our conceptual modelling approach, we identified four semiotic relations, associated with the four master tropes that have been claimed to provide a system to fully grasp the world concep-