Different kinds of sensors compose a meteorological observation system that measures meteorological variables. Sensors can collect data for a long period of time in a high sampling frequency. Some meteorological parameters can be determined by making measurements that ranges from a few seconds to annual measurements which depends on the kind of equipment and application needs. In this scenario, data management is not a trivial task due to heterogeneity, large amount of data and also to the usage of proprietary software for data gathering and handling. We used a data acquisition system (datalogger) to collect and store data from a thermo-baro-hygrometer, and a pyranometer, which were calibrated previously in the laboratory. This paper aimed to analyze the open source Elasticsearch, Logstash and Kibana (ELK) stack to capture, transform, enrich, store, index, select relevant time slots and generate graphs that were integrated in a dashboard for combined visualization and analysis. Additionally, we explored its capacity to embed metadata from sensors and correct data based on a calibration certificate, also showing some relevant graphics. In this weather application, we observed that this set of computational tools are well suited to manage the daily difficulties in handling meteorological data and metadata.

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Meteorological observation systems are extremely data-driven. However, several factors affect measurements, which require the use of environmental metrology techniques to increase the quality of measurements, decrease errors and evaluate measurements uncertainty. In this paper, we propose and develop a framework that integrates, process and visualizes sensor data and its associated metadata (for rainfall monitoring). This task is accomplished with a workflow designed to correct raw sensor data, which uses an elastic stack based infrastructure to collect, transform, and store sensor data and metadata. We validated our framework using real precipitation data from a Tipping Bucket Rain Gauge.

ABSTRACTeScience research, in computer science, concerns the development of tools, models and techniques to help scientists from other domains to develop their own research. One problem which is common to all fields is concerned with the management of heterogeneous data, offering multiple interaction possibilities. This paper presents a proposal to help solve this problem, tailored to wireless sensor data -an important data source in eScience. This proposal is illustrated with a case study.

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