Visual Analytics Improving Data Understandability in IoT Projects: An Overview of the U. S. DOE ARM Program Data Science Tools
André Filipe de Moraes Batista, Pedro Luiz Pizzigatti Corrêa, Giri Palanisamy · 2016
The IoT advancement generates a massive volume and a variety of data at unprecedented velocity. There is a need to interpret and to convert such data into valuable information, enabling the discovery of knowledge as well as the development of new technologies and data products. From the Data Science research field, Visual Analytics (VA) techniques seek to support in the analysis and understanding of large datasets using statistical techniques and data mining, aided by visualization techniques. This paper demonstrates how VA techniques can be applied in Data Science projects enabling users to reason over a dataset generated by IoT devices, what we call as Data Understandability. A practical case is presented in order to provide an overview of the data management procedures and how a set of Visual Analytics tools developed in the context of The U. S. Department of Energy Atmospheric Radiation Measurement (ARM) Program are helping users to visualize and analyze the collected data from a climate sensor network spread throughout the world.