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Water management field has concentrated great interest, with the potential to affect the long term well-being, the societal economy and security. In parallel, it imposes specific research challenges which have not been already met, due to the lack of fine-grained data. Knowledge extraction and decision making for efficient management in the energy field has attracted a lot of interest in Big Data research. However, the water domain is strikingly absent, with minimal focused work on data exploitation and useful information extraction. The goal of this work is to discover persistent and meaningful knowledge from water consumption data and provide efficient and scalable big data management and analysis services. We propose a novel methodology which exploits machine learning techniques and introduces a robust probabilistic classifier which is able to operate on data of arbitrary dimensionality and of huge volume. It …