Many advances in science come from observing previously unobserved phenomena. To observe such phenomena in high spatio-temporal resolution, environmental scientists, for example, have started using densely deployed (typically wireless) sensor networks. Such sensors are capable of revealing the complex interactions between atmospheric and land surface components with enough precision to generate accurate environmental system models. This high-resolution sensing, however, poses many big challenges for scientists. For example, various data management problems such as reliably archiving large volumes (e.g., many terabytes) of data, querying and visualizing it, sharing it with others with access control policies, and maintaining sufficient context and provenance of sensor data often pose significant overhead for scientists . Moreover, due to high costs of sensors, scientists with limited resources are often restricted to use data collected from a small number of sensors that are not sufficient to provide accurate environmental system models.<\/p>\n","protected":false},"excerpt":{"rendered":"
Many advances in science come from observing previously unobserved phenomena. To observe such phenomena in high spatio-temporal resolution, environmental scientists, for example, have started using densely deployed (typically wireless) sensor networks. Such sensors are capable of revealing the complex interactions between atmospheric and land surface components with enough precision to generate accurate environmental system models. 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