Seoul Weather is a weather application that reports optimal exercise times via Twitter, created by Haesung Lee. More specifically, Seoul Weather’s goal is to identify and report the optimal time to exercise outdoors — which factors in the time of day with the least particulate matter (PM) pollution in the air, as well as other factors.
Seoul Weather used InfluxDB as its purpose-built time series database, alongside a number of other products such as Amazon Web Services (AWS), Node-RED (Node.js) and Jupyter Notebook, as well as Twitter, to predictively analyze machine data using machine learning algorithms.
Haesung found that InfluxDB conveniently stored weather and pollution data, as well as IoT metrics collected in real-time through AWS IoT. He appreciated how simple it was to visualize the data in conjunction with Grafana, which made it easier to analyze the time series data with machine learning algorithms and report weather and pollution data accurately.
Helping optimize citizens’ exercise times
By collecting air, temperature and pollution data
Enabling ML models
To improve the predictive analysis of weather conditions
Better IoT monitoring
Used InfluxDB to collect weather and sensor metrics data in real time
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