A big data analytics design patterns to select customers for electricity theft inspection

The complexity of Big Data originates not exclusively from high volume, but also from high velocity and high variety. These characteristics are caused by technological advancements, mainly those that (1) allow to store huge quantities of data, and that facilitate easy access and data manipulation; and (2) those that now allow processing these data for more comprehensive insight. Particularly these are techniques that facilitate incorporation and combined analysis of heterogeneous data sources. The first category gave rise to the concepts of open databases, interactive web services, and social media. These are by themselves huge sources of data from which insight may be generated if the obstacles of their highly heterogeneous, dynamic and decentral nature – common characteristics of socio-technical systems – can be overcome. Building upon systems engineering principles, this paper presents a design pattern that pays respect to the inter-disciplinary challenges of the Big Data environment. At its core are a functional architecture and a phased advancement model. These are being elucidated exemplarily by outlining the development of a Big Data analytics approach to select suspicious customers from the customer database of a power grid operator for inspections on electricity theft, based on customer profiling.
LEAL, Adriano Galindo; BOLDT, Marcel. A big data analytics design patterns to select customers for electricity theft inspection. In: PES TRANSMISSION AND DISTRIBUTION CONFERENCE AND EXPOSITION LATIN AMERICA, 8., 2016, Morelia, Mexico. Proceedings… 6p 

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