Enhancing Explanation Generation in the CaJaDE system using Interactive User Feedback Lee, Juseung Computer science CaJaDE data provenance Database management explanations user feedback user interaction In today’s data-driven world, it is becoming increasingly difficult to interpret and understand query results after going through several manipulation steps, especially on a large database. There is a need for automated techniques that explain query results in a meaningful way. A recent study, CaJaDE(Context-Aware Join-Augmented Deep Explanations), presents a novel approach to generating explanations of query results including crucial contextual information. However, it becomes difficult to interpret explanations since the search space increases exponentially.In this thesis, we propose a new approach that introduces a user interaction model for a purpose of enhancing the generation of explanations in the CaJaDE system. We implemented a user interaction model that consists of three modules: User Selection, Recommendation Score, and User Rating. With these modules, our approach guides a user while exploring relevant join graphs, and lets them be involved in the decision-making process while generating join graphs. We demonstrate through performance experiments and user study that our approach is an effective method for users to understand explanations. Glavic, Boris 2022 Thesis application/pdf islandora:1024891 http://hdl.handle.net/10560/islandora:1024891 Illinois Institute of Technology CS / Computer Science en In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access