Categorical Anomaly Detection, in Real-time

Only thatDot Anomaly Detection can analyze categorical data to provide real-time anomaly detection that differentiates truly novel data, reducing false positives, and providing an explanation why.

Real-time Streaming Anomaly Detection As An API

No batch operations. Score streaming data for novelty as it is ingested to enable real-time use cases.

Categorical Data

Only thatDot supports categorical data types; such as file names, identifiers and strings.

Contextual Analysis

Conditional probabilities and graphical model-based novelty scoring allow thatDot Anomaly Detector to use context to reduce false positives.

Simple Operation

No labelling of data or tuning of hyper-parameters required. Simply start the system, and it works immediately.

Full Anomaly Context

Every component of every observation is scored for novelty, providing deep insight.

Visualization UI

View a graphical model of your data to understand its shape and distribution.

Use Context To Reduce False Positives

thatDot Anomaly Detector dramatically reduces false positives by using contextual information—learned automatically from the data.

Typical anomaly detection is a very mathematical process, requiring exclusively numeric data-which can provide anomaly scores for each item in a batch, but only when processed together. thatDot Anomaly Detector uses the structure created from all observations to determine whether the observation is anomalous given the relevant context, in real-time.

The image shows the scores of real-world data streamed into thatDot Anomaly Detector. The color scale on the right shows how unique each observation is, the yellow representing entirely unique observations that have never been seen before. Instead of just labeling all unseen observations as anomalous, thatDot Anomaly Detector uses a rich graphical model of the data to integrate context into the anomaly score. Yellow dots with low scores (near the bottom of the plot), are examples of unique data that are not anomalous. “New” is not always “novel”. Only thatDot Anomaly Detector can automatically use context to learn the difference.

Sample Use Cases

Operational Security

Monitor SaaS and IaaS usage for unusual configuration changes or resource access patterns. 

Network Optimization

Identify network route inefficiencies by discovering anomalous traffic behavior.

E-Commerce Intelligence

Analyze transactions for suspicious activity, or to recommend common alternative products—all in real-time.

Log Volume Reduction

Actively filter logs and events for novelty to reduce log storage costs and log analysis licensing.

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 Connect 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.