ViewSeeker: An interactive view recommendation tool

View recommendation has emerged as a powerful tool to assist data analysts in exploring and understanding big data. Existing view recommendation approaches proposed a variety of utility functions in selecting useful views. Even though each utility function might be suitable for specific scenarios, identifying the most appropriate ones along with their tunable parameters, which represent the user’s intention during an exploration, is a challenge for both expert and non-expert users. This paper presents the first attempt towards interactive view recommendation by automatically discovering the most appropriate view utility functions in an exploration based on the user’s preferences. In particular, our proposed ViewSeeker uses a novel active learning method to discover the view utility function by interactively refining the set of k view recommendations. The effectiveness and efficiency of ViewSeeker was experimentally evaluated using both synthetic and real data sets.