Best practices for architecting visual exploratory Data Analytics applications (2018)
Data Analytics is one of the activities that is currently booming due to the increasing use of advanced techniques such as Machine Learning and the constant growth of computational capacity. Because of this, it is possible to obtain insights from large volumes of data, make decisions in real time and anticipate changes and trends in the markets. Although with current technologies it is possible to explore large volumes of data, the challenge remains to decrease the computational complexity to manipulate small and large volumes of data interactively. This thesis contributes to review the current panorama of approaches and proposals that have originated in the Visual Analytics and Data Integration communities, the current technologies that allow storing and manipulating currently large volumes of data. Also, this thesis records the results of different benchmarking tests whose purpose was to determine the technologies that allow to explore and visualize large volumes of data in the shortest possible time. Based on these results, this thesis proposes recommendations on how to design applications for the exploration and visualization of multivariable and streaming data.