Context-sensitive framework for visual analytics in energy production from biomass

Abstract

Data masses require a lot of data processing. Data mining is the traditional way to convert data into knowledge. In visual analytics, humans are integrated into the process as there is continuous interaction between the analyst and the analysis software. Data mining methods can be utilized also in visual analytics where the priority is given to the visualization of the information and to dimension reduction. However, the provided data is not always enough. There is a large amount of background contextual information, which should be included into the automated process. This paper describes a context-sensitive approach, in which we utilize visual analytics by studying all phases in the process according to our ”sensing, processing and actuation” framework. Experimental studies show that our framework can be very useful in the process of analyzing causes for and relations between variable changes with laboratory-scale power plant data.