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From Internet of Things Data to Business Processes: Challenges and a Framework
This work proposes a framework to semi-automatically perform a set of structured steps to convert low-level IoT sensor data into higher-level process events that are suitable for process mining.
End-To-End Self-tuning Self-supervised Time Series Anomaly Detection
A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.)
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors
IoT data is a central element in the successful digital transformation of agriculture.
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