For cardiac surgeons, mitral valve reconstruction (MVR) surgery is a highly demanding procedure, where an artificial annuloplasty ring is implanted onto the mitral valve annulus to re-enable the valve's proper closing functionality. For a successful operation the surgeon has to keep track of a variety of relevant impact factors, such as patient-individual medical history records, valve geometries, or tissue properties of the surgical target, and thereon-based deduce type and size of the best-suitable ring prosthesis according to practical surgery experience. With this work, we aim at supporting the surgeon in selecting this ring prosthesis by means of a comprehensive information processing pipeline. It gathers all available patient-individual information, and mines this data according to 'surgical rules', that represent published MVR expert knowledge and recommended best practices, in order to suggest a set of potentially suitable annuloplasty rings. Subsequently, these rings are employed in biomechanical MVR simulation scenarios, which simulate the behavior of the patient-specific mitral valve subjected to the respective virtual ring implantation. We present the implementation of our deductive system for MVR ring selection and how it is integrated into a cognitive data processing pipeline architecture, which is built under consideration of Linked Data principles in order to facilitate holistic information processing of heterogeneous medical data. By the example of MVR surgery, we demonstrate the ease of use and the applicability of our development. We expect to essentially support patient-specific decision making in MVR surgery by means of this holistic information processing approach.