Enhancing Big Data Exploration with Faceted Browsing

This article presents a new approach to visualize huge amount of data, based on a Bayesian suggestion algorithm and the widely used enterprise search platform Solr, and demonstrates how the proposed Bayesian suggest algorithm became a key ingredient in a big data scenario, where generally a query can generate so many results that the user can be confused.

Abstract

Big data analysis now drives nearly every aspect of modern society, from manufacturing and retail, through mobile and financial services, through the life sciences and physical sciences. The ability to continue to use big data to make new connections and discoveries will help to drive the breakthroughs of tomorrow. One of the most valuable means through which to make sense of big data, and thus make it more approachable to most people, is data visualization. Data visualization can guide decision-making and become a tool to convey information critical in all data analysis. However, to be actually actionable, data visualizations should contain the right amount of interactivity. They have to be well designed, easy to use, understandable, meaningful, and approachable. In this article, we present a new approach to visualize huge amount of data, based on a Bayesian suggestion algorithm and the widely used enterprise search platform Solr. We demonstrate how the proposed Bayesian suggestion algorithm became a key ingredient in a big data scenario, where generally a query can generate so many results that the user can be confused. Thus, the selection of the best results, together with the result path chosen by the user by means of multifaceted querying and faceted navigation, can be very useful.