Abstract
More data from various sources enable in-depth analysis of various network parameters. Efficient analysis requires the use and adaption of methods developed for big data applications, like MapReduce for parallel in-database processing. This chapter investigates the applicability of data-driven methods and interactive data visualization to discover new insight into low-voltage network states. The use of MapReduce functions based on Open Source Software like R or Java is demonstrated in combination with a commercial distributed analytics database. These customized functions are applied to analyze unbalance voltage conditions in low-voltage networks and discover and explore the reasons by relating it to other events in the network. The discovery process is supported by interactive visualization methods, like affinity graphs for representing collaborative filters. Performance comparisons to conventional database concepts are discussed at the end of the chapter.
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