Association for Computing Machinery
Interactive Data Exploration (IDE) is a key ingredient of a diverse set of discovery-oriented applications, including ones from scientific computing and evidence-based medicine. In these applications, data discovery is a highly ad hoc interactive process where users execute numerous exploration queries using varying predicates aiming to balance the trade-off between collecting all relevant information and reducing the size of returned data. Therefore, there is a strong need to support these human-in-the-loop applications by assisting their navigation in the data to find interesting objects. In this paper, the authors introduce AIDE, an Automatic Interactive Data Exploration framework that iteratively steers the user towards interesting data areas and \"Predicts\" a query that retrieves his objects of interest.