Real-time Anomaly Detection With Actionable Context
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.
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.
thatDot Novelty Detector is a new AI technique built on thatDot Connect, enabling 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.
Simple Deployment & Use
thatDot Novelty Detector is an easily deployed, highly scalable application. Spin up an AWS or on-premise instance in minutes.
A Full Featured Application
- On-premise or cloud installed .jar file or container (not SaaS)
- APIs for streaming or batch data ingestion
- Self-training operation, no data labeling
- Built in data transformation functions
- A single 8-core instance scales to 15,000 observations/sec.
- Integrated and online self-service documentation
Real-time Actionable Insights
- Real-time individual or bulk anomaly detection at scale
- Build a detailed behavioral fingerprint for deep contextual understanding that eliminates false-positives
- Every observation response includes “why” an anomaly was identified
- Detailed scores for novelty, uniqueness, Information Content, probability…
- Integrate into your code to drive real-time remediation workflows
Monitor SaaS and IaaS usage for unusual configuration changes or resource access patterns.
Identify network route inefficiencies and eliminate redundant alerts through topology awareness.
Analyze usage for excess concurrent usage, and generate events to enforce entitlement compliance.
Actively filter logs and events for to identify the logs worth analysis, and the bulk that are not.
Need To Transform And Join Data First?
Many of the most valuable observations are the result of transforming, combining and interpreting data from many different sources. thatDot 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’s native format, no conversions to numeric values needed.