Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most "useful" or "interesting". The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.

Water, sanitation, and hygiene interventions have varying effectiveness in reducing fecal contamination in the domestic environment; delivering them in combination could yield synergies. We conducted environmental assessments within a randomized controlled trial in Bangladesh that implemented single and combined water treatment, sanitation, handwashing (WSH) and nutrition interventions (WASH Benefits, NCT01590095). After one and two years of intervention, we quantified fecal indicator bacteria in samples of drinking water (from source or storage), child hands, children's food and sentinel objects. In households receiving single water treatment interventions, Escherichia coli prevalence in stored drinking water was reduced by 50% and concentration by 1-log. E. coli prevalence in food was reduced by 30% and concentration by 0.5-log in households receiving single water treatment and handwashing interventions. Combined WSH did not reduce fecal contamination more effectively than its components. Interventions did not reduce E. coli in groundwater, on child hands and on objects. These findings suggest that WSH improvements reduced contamination along the direct transmission pathways of stored water and food but not along indirect upstream pathways. Our findings support implementing water treatment and handwashing to reduce fecal exposure through water and food but provide no evidence that combining interventions further reduces exposure.