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Anomalies are deviations from expected patterns and can occur in a variety of environments—whether in banking transactions, industrial operations, the marketing industry, or healthcare monitoring. Traditional detection methods often produce high false alarm rates. A false positive occurs when a system incorrectly identifies a routine event as an anomaly, resulting in unnecessary investigation efforts and operational delays. This inefficiency is a pressing problem because it drains resources and diverts attention from the real problems that need to be solved. This article takes an in-depth look at a specialized approach to anomaly detection that makes extensive use of rule-based engines. This approach improves the accuracy of identifying violations by cross-referencing multiple key performance indicators (KPIs). Not only can this approach more effectively verify or disprove the presence of an anomaly, but it can sometimes also isolate and identify the root cause of the problem.
This is the continuous data flow reviewed by the engine. Each point in the flow may be associated with one or more KPIs that are used by the rules engine to evaluate against its training ruleset. A continuous flow of data is essential for real-time monitoring, providing the engine with the necessary information to work.
The heart of the system is the rules engine, which needs to be trained to understand the nuances of the KPIs it will monitor. This is where a set of KPI rules comes into play. These rules serve as the algorithmic basis of the engine and are designed to correlate two or more KPIs together.
Type of KPI rules:
After receiving the data, the engine immediately looks for deviations or anomalies in the incoming KPIs. An anomaly here refers to any metric that falls outside a predetermined acceptable range. The engine flags these anomalies for further investigation, which can be divided into three main operations: accept, reject, and narrow down. This may involve correlating one KPI with another to validate or negate a detected anomaly.
The basic steps involve creating a series of KPIs that relate multiple KPIs to each other associated rules. For example, a rule might relate product quality metrics to production speed in a factory setting. For example:
The rule engine has been comprehensively trained and can effectively Apply these rules.
The rules engine proactively monitors incoming data, applying its trained rules to identify anomalies or potential anomalies.
In identifying potential anomalies, the engine:
This article outlines an approach to anomaly detection using a rules engine trained on various KPI rule sets. In contrast to traditional anomaly detection systems, which often rely solely on statistical algorithms or machine learning models, this approach uses a specialized rules engine as its cornerstone. By delving deeper into the relationships and interactions between different KPIs, businesses can gain more granular insights that simple, stand-alone metrics cannot provide. This enables more robust strategic planning, better risk management, and an overall more effective approach to achieving business goals. Once an anomaly is flagged, the engine compares it to other associated KPIs using its pre-trained KPI rules. The point here is to determine whether the anomaly is actually a problem or just an outlier.
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