Category Archives: Anomaly Detection

Contextual Outlier Detection with Statistical Modeling on Spark

Sometimes an outlier is defined with respect to a context. Whether a data point should be labeled as an outlier depends on the associated context. For a bank ATM, transactions that are considered normal between 6 AM and 10 PM, … Continue reading

Posted in Anomaly Detection, Big Data, Data Science, Spark | Tagged , , | Leave a comment

Alarm Flooding Control with Event Clustering Using Spark Streaming

You show up at work in the morning and open your email to find 100 alarm emails in your inbox for the same error from an application running on some server within a short time window of 1 minute. You … Continue reading

Posted in Anomaly Detection, Big Data, Real Time Processing, Spark, stream processing | Tagged , , , | 1 Comment

Anomaly Detection with Robust Zscore

Anomaly detection with with various statistical modeling based techniques are simple and effective. The Zscore based technique is one among them. Zscore is defined as the absolute difference between a data value and it’s mean normalized with standard deviation. A … Continue reading

Posted in Anomaly Detection, Big Data, data quality, Data Science, Hadoop and Map Reduce | Tagged , , | 8 Comments