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 building an ensemble analytical rule using reusable operators, the method including:
connecting, by a rule instantiator, a selected ensemble analytical rule to a metric stream of performance information of a network, wherein:
the ensemble analytical rule processes a time series of metrics through multiple stages of reusable operators, at least one of the reusable operators applying two or more inputs;
at least one stage of the ensemble analytical rule quantizes one or more results from an earlier stage of the ensemble analytical rule into comparison values including at least normal and non-normal values; and
two or more operator determined parameters of two or more of the reusable operators in the multiple stages are initially determined by user entry or user acceptance of default values;
analyzing, by a rule engine, the metric stream by applying the ensemble analytical rule;
after the analysis of the metric stream by applying the ensemble analytical rule, the rule engine, an alert scorer and/or an alert tuner:
feeding back the quantized results for expert analysis,
receiving identifications of one or more type I or type II errors in the quantized results, and
automatically adjusting one or more of the parameters of the reusable operators to correct the identified errors; and
further analyzing the metric stream by applying, by the rule engine, the ensemble analytical rule with the automatically adjusted parameters.
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 the metric stream measures latency of a first network computing device responding to a message from a second network computing device.
5. A system including one or more processors coupled to memory, the memory loaded with computer instructions to build an ensemble analytical rule using reusable operators, the computer instructions, when executed on the processors, implement actions comprising:
connecting, by a rule instantiator, a selected ensemble analytical rule to a metric stream of performance information of a network, wherein:
the ensemble analytical rule processes a time series of metrics through multiple stages of reusable operators, at least one of the reusable operators applying two or more inputs;
at least one stage of the ensemble analytical rule quantizes one or more results from the ensemble analytical rule into comparison values including at least normal and non-normal values; and
two or more operator determined parameters of two or more of the reusable operators in the multiple stages are initially determined by user entry or user acceptance of default values;
analyzing, by a rule engine, the metric stream by applying the ensemble analytical rule;
after the analysis of the metric stream by applying the ensemble analytical rule, at least one of the rule engine, an alert scorer and/or an alert tuner:
feeding back the quantized results for expert analysis,
receiving identifications of one or more type I or type II errors in the quantized results, and
automatically adjusting one or more of the parameters of the reusable operators to correct the identified errors; and
further analyzing the metric stream by applying, by the rule engine, the ensemble analytical rule with the automatically adjusted parameters.
6. The system of claim 5 , further implementing actions transmitting an alert to a human operator when non-normal comparison values are repeatedly detected over a predetermined time.
7. The system of claim 5 , further implementing actions transmitting an alert to a human operator when repeated normal comparison values are repeatedly detected over a predetermined time.
8. The system of claim 5 , wherein the metric stream measures latency of a first network computing device responding to a message from a second network computing device.
9. A non-transitory computer readable storage medium impressed with computer program instructions to build an ensemble analytical rule using reusable operators, the computer program instructions, when executed on a processor, implement a method comprising:
connecting, by a rule instantiator, a selected ensemble analytical rule to a metric stream of performance information of a network, wherein:
the ensemble analytical rule processes a time series of metrics through multiple stages of reusable operators, at least one of the reusable operators applying two or more inputs;
at least one stage of the ensemble analytical rule quantizes one or more results from an earlier stage of the ensemble analytical rule into comparison values including at least normal and non-normal values; and
two or more operator determined parameters of two or more of the reusable operators in the multiple stages are initially determined by user entry or user acceptance of default values;
analyzing, by a rule engine, the metric stream by applying the ensemble analytical rule;
after the analysis of the metric stream by applying the ensemble analytical rule, at least one of the rule engine, an alert scorer and/or an alert tuner:
feeding back the quantized results for expert analysis,
receiving identifications of one or more type I or type II errors in the quantized results, and
automatically adjusting one or more of the parameters of the reusable operators to correct the identified errors; and
further analyzing the metric stream by applying, by the rule engine, the ensemble analytical rule with the automatically adjusted parameters.