Extracting Appliance Behavior from Heterogeneous Data

Machine learning has triggered a paradigm shift, facilitating the gathering of relevant information from a large amount of seemingly diverse data. The primary goal of this research paper is to emphasize the effectiveness of a varied set of data analysis methods used to extract meaningful intelligence from a complex data set gathered by experimenting with house-hold appliances in a controlled environment. The methods differ depending on the goal of the analysis and the characteristics of the data: machine learning, statistical modeling, and natural language processing. In this paper, we demonstrate how visual representations of the information can be a deciding factor when analyzing experimental data. The impact of computations on the semantic and structural form of the data, as a pre-processing step, is also detailed.