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 multi-faceted querying and faceted navigation, can be very useful.
|Data di pubblicazione:||2015|
|Titolo:||Big data exploration with faceted browsing|
|Autori:||Zhu, Song; Simonini, Giovanni|
|Digital Object Identifier (DOI):||10.1109/HPCSim.2015.7237087|
|Data del convegno:||20 July 2015 through 24 July 2015|
|Nome del convegno:||13th International Conference on High Performance Computing and Simulation, HPCS 2015;|
|Luogo del convegno:||Amsterdam; Netherlands;|
|Titolo del libro:||Proceedings of the 2015 International Conference on High Performance Computing and Simulation, HPCS 2015|
|Appare nelle tipologie:||Relazione in Atti di Convegno|
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