Tuning context-aware rule engine for anomaly detection
The technology disclosed relates to building ensemble analytic rules for reusable operators and tuning an operations monitoring system. In particular, it relates to analyzing a metric stream by applying an ensemble analytical rule. After analysis of the metric stream by applying the ensemble analytical rule, quantized results are fed back for expert analysis. Then, one or more type I or type II errors are identified in the quantized results, and one or more of the parameters of the operators are automatically adjusted to correct the identified errors. The metric stream is further analyzed by applying the ensemble analytical rule with the automatically adjusted parameters.
1. A method of using an ensemble analytical rule built using reusable operators, the method including:
obtaining an ensemble analytical rule with one or more parameters as automatically adjusted by:
analyzing, by a rule engine, a metric stream of performance information of a network selected for metrics of the metric stream based upon a metric class received from a metric classifier UI by applying the ensemble analytical rule to process a time series of metrics through multiple stages of reusable operators;
quantizing one or more results from the ensemble analytical rule into comparison values including at least normal and non-normal values;
providing the quantized results for expert analysis;
receiving identifications of one or more errors in the quantized results; and
automatically adjusting one or more parameters of the reusable operators to correct the identified errors; and
using the ensemble analytical rule with the one or more parameters as automatically adjusted to analyze metrics in a metric stream.
2. The method of claim 1 , further including transmitting an alert to a human operator when non-normal comparison values are repeatedly detected over a predetermined time.
3. The method of claim 1 , further including transmitting an alert to a human operator when repeated normal comparison values are repeatedly detected over a predetermined time.
4. The method of claim 1 , wherein at least one of the reusable operators applies two or more inputs selected from a set consisting of a metric stream and an output stream of another operator.
5. The method of claim 1 , wherein two or more operator determined parameters of two or more of the reusable operators in the multiple stages are initially determined from a user entry or a user acceptance of default values.
6. The method of claim 1 , wherein operators are organized into a decision tree rooted by a single operator output.
7. The method of claim 6 , further implementing actions of estimating using the decision tree, a severity of anomalous data points in the metric stream.
8. The method of claim 1 , wherein expert analysis includes application of one or more domain rules that incorporate specific and targeted expert domain knowledge optimized according to metric class of performance metrics.
9. The method of claim 1 , wherein the metric stream measures latency of a first network computing device responding to a message from a second network computing device.
10. A non-transitory computer readable storage medium impressed with computer program instructions to use an ensemble analytical rule built using reusable operators, the computer program instructions, when executed on one or more processors, implement a method comprising:
obtaining an ensemble analytical rule with one or more parameters as automatically adjusted by:
analyzing, by a rule engine, a metric stream of performance information of a network selected for metrics of the metric stream based upon a metric class received from a metric classifier UI by applying the ensemble analytical rule to process a time series of metrics through multiple stages of reusable operators;
quantizing one or more results from the ensemble analytical rule into comparison values including at least normal and non-normal values;
providing the quantized results for expert analysis;
receiving identifications of one or more errors in the quantized results; and
automatically adjusting one or more parameters of the reusable operators to correct the identified errors; and
using the ensemble analytical rule with the one or more parameters as automatically adjusted to analyze metrics in a metric stream.
11. A system of using an ensemble analytical rule built using reusable operators, the system including:
one or more processors executing stored computer program instructions that, when executed, cause the one or more processors to:
obtain an ensemble analytical rule with one or more parameters as automatically adjusted by:
analyzing, by a rule engine, a metric stream of performance information of a network selected for metrics of the metric stream based upon a metric class received from a metric classifier UI by applying the ensemble analytical rule to process a time series of metrics through multiple stages of reusable operators;
quantizing one or more results from the ensemble analytical rule into comparison values including at least normal and non-normal values;
providing the quantized results for expert analysis;
receiving identifications of one or more errors in the quantized results; and
automatically adjusting one or more parameters of the reusable operators to correct the identified errors; and
use the ensemble analytical rule with the one or more parameters as automatically adjusted to analyze metrics in a metric stream.
12. The system of claim 11 , further including transmitting an alert to a human operator when non-normal comparison values are repeatedly detected over a predetermined time.
13. The system of claim 11 , further including transmitting an alert to a human operator when repeated normal comparison values are repeatedly detected over a predetermined time.
14. The system of claim 11 , wherein at least one of the reusable operators applies two or more inputs selected from a set consisting of a metric stream and an output stream of another operator.
15. The system of claim 11 , wherein two or more operator determined parameters of two or more of the reusable operators in the multiple stages are initially determined from a user entry or a user acceptance of default values.
16. The system of claim 11 , wherein operators are organized into a decision tree rooted by a single operator output.
17. The system of claim 16 , further implementing actions of estimating using the decision tree, a severity of anomalous data points in the metric stream.
18. The system of claim 11 , wherein expert analysis includes application of one or more domain rules that incorporate specific and targeted expert domain knowledge optimized according to metric class of performance metrics.
19. The system of claim 11 , wherein the metric stream measures latency of a first network computing device responding to a message from a second network computing device.