Using Category Theory as a Basis for a Heterogeneous Data Source Search Meta-engine: The Prométhée Framework

Abstract

It is generally acknowledged that integration of large-scale information systems is a challenging problem. A domain that particularly encounters this problem currently is healthcare, where information systems tend to be heterogeneous and in constant evolution. A particular need for health professionals is the ability to ask medical questions across a heterogeneous information system, then visualise and analyse the results in a synthetic and coherent manner. In this paper, we present a case study of a heterogeneous data source search meta-engine framework, based on category theory. This framework addresses the problem of cross-interrogation of heterogeneous data sources, such as relational database management systems, documentary database systems, or collections of documents. It additionally attempts to address the problem of constant evolution of such information systems. The framework has been successfully applied to the biomedical data of the medical information system at the Institut Curie, a major French cancer care centre. Different aspects of this work are illustrated, such as the mathematical foundations of the Prométhée framework, the methodology used for its implementation, and the impact that Prométhée has encountered since its deployment in a hospital environment.