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Monitor network, device, and application usage for unusual configuration changes or 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.
Intelligently filter log data to eliminate the huge bulk of uninformative data, and highlight what’s important.
Black swan events and unknown unknowns can be extremely hard to detect since you don’t know what to look for. Novelty finds them and increases your knowledge about your data.
Filter away the bulk of irrelevant readings to spotlight potential problems for predictive maintenance, smart metering, asset monitoring, process automation, and improving customer experiences.
Key Features
When you don't know what pattern to look for in a data stream, you just want to find the exceptions, the occurrences that don't fit the norm, Novelty is simple, fast and accurate. No training or data labeling is required. It's completely unsupervised. It builds a detailed behavioral fingerprint for contextual understanding. Simply feed it data, and it will score every event according to how unusual it is in your data set, and will also explain WHY an event is novel.
New is not always novel. Novelty uses context to accurately tell the difference. In the diagram to the right, for example, yellow dots are unique, but sometimes one-of-a-kind events are normal. Only the ones with highest novelty scores at the top are genuine anomalies.
The built-in AI analyzes categorical data as is, even if it has high cardinality. There's no need to delay analysis to transform categorical data into sparse, bloated numeric data. It returns detailed scores for novelty, uniqueness, information content, and probability.
We benchmarked Novelty against a standard Isolation Forest algorithm on the same data on the same 8-CPU, 16 GB virtual machines. Isolation Forest managed as many as 500 events per second. With Novelty, you can score over 15,000 events per second.
Several data transformation functions are included in Novelty so you won't need a separate transformation layer before you can use it. Stream raw data in directly.
You can limit or remove the influence of highly novel observations you know are not important. Either 1. Remove individual or groups of observations; 2. Define a rolling window of observations; or 3. Delete entire contexts from the system.