VizCertify: A Framework for Secure Visual Data Exploration Recently, there have been several proposals to develop visual recommendation systems. The most advanced systems aim to recommend visualizations, which help users to find new correlations or identify an interesting deviation based on the current context of the userxe2x80x99s analysis. However, when recommending a visualization to a user, there is an inherent risk to visualize random fluctuations rather than solely true patterns: a problem largely ignored by current techniques. In this paper, we present VizCertify, a novel framework to improve the performance of visual recommendation systems by quantifying the statistical significance of recommended visualizations. The proposed methodology allows to control the probability of misleading visual recommendations using both classical statistical testing procedures and a novel application of the Vapnik Chervonenkis (VC) dimension towards visualization recommendation which results in an effective criterion to decide whether a recommendation corresponds to a true phenomenon or not.

Leveraging DataAnalysis Session Logs for Efficient, Personalized, Interactive View Recommendation View recommendation has been recently adopted to assist data analysts in better understanding the data. In order to recommend useful views, existing view recommendation approaches propose a variety of utility functions, each suitable for a different usage scenario. However, as the interestingness of a recommended view is userdependent, no single utility function can represent usersu0027 preferences and intentions in all cases. With the richly available choices for utility functions, identifying the most appropriate ones along with their tunable parameters remains a challenge even for expert users. To help identify the most appropriate utility function, existing works have made attempts in two different directions, 1) providing generic recommendations of utility functions by learning offline from historical logs, and 2) providing personalized recommendations of utility functions by learning from the interactions with each particular user. Both proposed approaches exhibit clear advantages and disadvantages. In this work, to benefit from both approaches, we device a novel hybrid interactive view recommendation solution, namely HolisticViewSeeker (HVS), that effectively combines the offline learning with the online interactive learning to provide personalized view recommendation. Our experimental evaluations conducted on realworld data show that HVS outperforms both stateoftheart online and offline approaches by a significant margin in multiple respects.