In DAIAD we develop water sensing technologies providing real-time and highly granular data regarding water consumption in domestic environments. This level of detail is unprecedented in the water domain and introduces technical challenges and opportunities not currently addressed in the management and analysis of water consumption data. The increase in data volume and velocity DAIAD provides, natively establishes water consumption data as a Big Data source.
Big Data, a term coined to describe novel technologies and challenges as a result of the massive, interconnected and highly valuable data produced from modern ICT systems, is already relevant for a number of business areas and scientific domains. Novel, cost-efficient and highly scalable means to manage and analyze data, result in extracting actionable insights and value from data at an unprecedented scale. DAIAD is the first FP7 project actively treating water consumption data as a Big Data source, a novelty which requires an adaptation of relevant technologies for water data, as well as development of methods to accommodate the specific needs of water stakeholders.
Towards this, we are researching new algorithms, analysis techniques and data management solutions, specifically addressing water consumption data as a Big Data source. These advances are integrated across DAIAD in order to further motivate consumers in water savings, extract knowledge from water consumption, and provide actionable insights to influence water use. A subset of our work has been accepted in the IEEE BigData 2015 Conference, titled “A MapReduce Based k-NN Joins Probabilistic Classifier“.
We are really happy for this, because we are bringing closer the ICT and Water domains, highlighting areas of common research interest, while paving the way for future collaborations. Looking forward to the Conference and all opportunities for discussing the vision of DAIAD and the data-intensive challenges of water consumption!