Effective Big Data Visualization

Abstract

In the last several years, big data analytics has found an increasing role in our everyday lives. Data visualization has long been accepted as an integral part of data analytics. However, data visualization systems are not equipped to handle the complexities typically found in big data. Our work examines effective ways of visualizing big data, while also realizing that most visualization processes are interactive. During an interactive visualization session, an analyst issues several visualization requests, each of which builds on prior visualizations. In our approach, we integrate a distributed data processing system that can effectively process big data with a visualization system that can provide effective interactive visualization but for smaller amounts of data. The analyst's current request is used to infer contextual information about the analyst such as their expertise and tolerance for delay. This information is used to carefully determine additional data that can be sent to the visualization system for decreasing the response time for future requests, thus providing a better experience for the analyst and increasing their productivity.