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Automated early detection of faulty sensor data and semi-automated correction recommendations lead to a 50% reduction in manual effort for data correction.
Die automatisierte Früherkennung fehlerhafter Sensordaten und halbautomatische Korrekturempfehlungen führen zu einer 50%igen Reduzierung des manuellen Aufwands für die Datenkorrektur.
Critical infrastructure requires the highest level of data quality. But outdoor sensors are exposed to rough environments and not always deliver the most accurate information. Dirt, aging of sensors and vandalism are typical reasons for data errors. Manual data correction requires a lot of experience, focus and time. It does not scale well with the increasing number of sensors installed. Can smart algorithms come to the rescue?