Existing anomaly detection techniques rely on numerical data and threshold analysis, which breaks down in the face of high data dimensionality and produces high volumes of false-positives. thatDot Novelty Detector uses
categorical data to build a comprehensive behavioral fingerprint of your data. This deep contextual understanding eliminates false-positives and provides
WHY an anomaly was identified, making it immediately actionable.
Fewer False Positives
1000x Scaling Performance
thatDot Novelty Detector uses the categorical data in logs and events to build a rich behavioral fingerprint. Yellow dots with low scores (lower on the plot), are examples of unique data that are not anomalous. “New” is not always “novel”. Only thatDot Novelty Detector automatically uses context to learn the difference.
Novelty Detector is a new A.I. technique built on thatDot's Quine streaming graph. Novelty Detector allows for real-time use cases not possible with existing anomaly detection solutions. The performance comparison was made on identical 8-core, 16 GB, VMs. Note the different left and right hand scales.
Many of the most valuable observations are the result of transforming, combining and interpreting data from many different sources. thatDot's
Quine streamlines the process of real-time data cleaning and transformation, including the multiplexing of log, event and metrics data into any desired data format. And, with all thatDot solutions, you can use non-numeric data in it is native format, no conversions to numeric values needed.
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