CAPTO - A method for understanding problem domains for data science projects

Abstract

Data Science aims to infer knowledge from facts and evidence expressed from data. This occurs through a knowledge discovery process (KDD), which requires an understanding of the application domain. However, in practice, not enough time is spent on understanding this domain, and consequently, the extracted knowledge may not be correct or not relevant. Considering that understanding the problem is an essential step in the KDD process, this work proposes the CAPTO method for understanding domains, based on knowledge management models, and together with the available/acquired tacit and explicit knowledge, proposes a strategy for construction of conceptual models to represent the problem domain. This model will contain the main dimensions (perspectives), aspects and attributes that may be relevant to start a data science project. As a case study, it will be applied in the Type 2 Diabetes domain. Results show the effectiveness of the method. The conceptual model, obtained through the CAPTO method, can be used as an initial step for the conceptual selection of attributes.