Interactive data exploration based on user relevance feedback

Interactive Data Exploration (IDE) applications typically involve users that aim to discover interesting objects by it-eratively executing numerous ad-hoc exploration queries. Therefore, IDE can easily become an extremely labor and resource intensive process. To support these applications, we introduce a framework that assists users by automatically navigating them through the data set and allows them to identify relevant objects without formulating data retrieval queries. Our approach relies on user relevance feedback on data samples to model user interests and strategically collects more samples to refine the model while minimizing the user effort. The system leverages decision tree classifiers to generate an effective user model that balances the trade-off between identifying all relevant objects and reducing the size of final returned (relevant and irrelevant) objects. Our preliminary experimental results demonstrate that we can predict linear patterns of user interests (i.e., range queries) with high accuracy while achieving interactive performance.