Volume XV |

Smart Data for ICT-based Water Management

Abstract: Water is an essential, limited and sensitive life resource, and it is in focus of various persons or groups, from simple citizens to decision persons at country/world level, and, of course, also of scientists from different research fields. Water resource dynamic consequences exceed watersheds or water systems. Due to the support of new technologies, researches like people, water, and climate: adaptation and resilience in agricultural watersheds, developed a better understanding of the processes that link global-scale climate and socioeconomic drivers to regional-scale responses in land use decision-making, water quality, and water quantity. Recently, Cloud Computing emerged as the de facto state-of-the-art for data analytics. We require optimized platforms to co-locate data and computation and therefore mitigate the network bottleneck when moving data. However, as data may not be equally distributed across sites and since intermediate data are required to be aggregated to produce results, Cloud computing platforms may suffer severe performance degradation in such distributed settings. Thus, in our research activities we intend to address smart data extraction for water resource management, to explore new data distribution techniques and decision support systems that can co-operatively deal with distributed big data processing for single and multiple concurrent applications. Another challenging issue is to provide real-time analysis of shared and distributed data. While most real-time processing engines can efficiently benefit of the un-debatable performance of in-memory processing, they don’t consider the data management during data processing (i.e. where to store the intermediate temporary data) or dependencies in-between processed data, which are common in environmental applications. In this case, mathematical models represent suitable instruments used in prediction and prognosis model for different parameters (i.e. water quality index), which are important for decision support systems for water resource management.