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Outlier Detection Jian Pei Simon Fraser UniversityOumuamua 2Outlier Detection In 2017, an astronomical event occurred that was unlike any other: for the first time, we observed an object that we are
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Online outlier detection is a process used to identify unusual patterns in data as it is collected, enabling real-time analysis of data streams.
Who is required to file online outlier detection over?
Organizations and individuals who collect and process large volumes of data that may include anomalies are typically required to implement online outlier detection.
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To fill out online outlier detection, one must configure the detection algorithms, input relevant data parameters, and set thresholds for identifying outliers in data.
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The purpose of online outlier detection is to quickly identify and respond to anomalies in real-time data, which can prevent errors and improve data quality.
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Information such as detected outliers, timestamps, affected data points, and any corrective actions taken must be reported in online outlier detection.
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