Social network analysis (SNA) is a core pursuit of analyzing social networks today. In addition to the usual statistical techniques of data analysis, these networks are investigated using SNA measures. It helps in understanding the dependencies between social entities in the data, characterizing their behaviors and their effect on the network as a whole and over time. Therefore, this article attempts to provide a succinct overview of SNA in diverse topological networks (static, temporal, and evolving networks) and perspective (ego‐networks). As one of the primary applicability of SNA is in networked data mining, we provide a brief overview of network mining models as well; by this, we present the readers with a concise guided tour from analysis to mining of networks. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Commercial, Legal, and Ethical Issues > Social Considerations

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Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.