Towards Autonomous Exploratory Data Analysis

In the era of Big Data, exploratory data analysis (EDA) is gaining great importance. Yet, EDA is still a difficult process, especially for non-expert users, as it requires deep understanding of the investigated domain and the particular context. Consequently, users may skip significant analysis actions and overlook important aspects of the data. In this research, we aim to set the ground for employing Deep Reinforcement Learning (DRL) techniques in the context of EDA. We suggest an end-to-end framework architecture coupled with an implementation of each component. Our goal is the development of DRL models and techniques for facilitating a full-fledged, autonomous solution for EDA.