TY - JOUR TI - A data driven recommender framework DO - https://doi.org/doi:10.7282/T3CR5XGF PY - 2017 AB - Process mining has been receiving a large amount of scrutiny by researches and industry personnel alike in the recent past. The process logs and traces left by business process can be a big source of information and knowledge about the behavioral aspects of the process. Process mining techniques help extract the knowledge from the traces and logs. This knowledge based data coupled with process mining can used to design recommendations for a user. Most of the existing recommender systems have not been developed based on process mining algorithms and hence provide a whole new scope for research and improvement in their development. Therefore, in this thesis we propose a novel data driven recommender model that can provide recommendations of sequential procedural steps, visualizations and diagnostics for a specific group of patients to provide a feasible and more accurate solution to the problem. KW - Electrical and Computer Engineering LA - eng ER -