Enhancing big data exploration with faceted browsing

With the modern information technologies, data availability is increasing at formidable speed giving raise to the Big Data challenge (Bergamaschi, 2014). As a matter of fact, Big Data analysis now drives every aspect of modern so- ciety, such as: manufacturing, retail, financial services, etc., (Labrinidis & Jagadish, 2012). In this scenario, we need to rethink advanced and efficient human-computer-interaction to be able to handling huge amount of data. In fact, one of the most valuable means to make sense of Big Data, to most peo- ple, is data visualization. As a matter of fact, data visualization may guide decision-making and become a powerful tool to convey information in all data analysis tasks. However, to be actually actionable, data visualization tools should allow the right amount of interactivity and to be easy to use, under- standable, meaningful, and approachable. In this article, we present a new approach to visualize and explore a huge amount of data. In particular, the novelty of our approach is to enhance the faceted browsing search in Apache Solr∗ (a widely used enterprise search platform) by exploiting Bayesian networks, supporting the user in the explo- ration of the data. We show how the proposed Bayesian suggestion algorithm (Cooper & Herskovits, 1991) be a key ingredient in a Big Data scenario, where a query can generate too many results that the user cannot handle. Our pro- posed solution aim to select best results, which together with the result-path, chosen by the user by means of multi-faceted querying and faceted navigation, can be a valuable support for both Big Data exploration and visualization. In the fellowing, we introduce the faceted browsing technique, then we de- scribe how it can be enhanced exploiting Bayesian networks.