US 7,783,745 B1Granted

Defining and monitoring business rhythms associated with performance of web-enabled business processes

AssigneeEntrust Corporation

InventorsSunil Bhargava, Amitava K. Raha

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Abstract

Monitoring the operational performance of a network-based business service involves defining and detecting significant variances in activities associated with performance of the service. A business service is characterized by corresponding business rhythms, which are derived from patterns of metric values for business activities that are part of business processes corresponding to the business service. Each business rhythm is characterized as a set of statistics about the corresponding metric(s) classified for a period of time or over a group of multiple periods of time, statistically compressed, and persistently stored. For purposes of real-time monitoring of the operational performance of the business service, significant variances in the normal behavior of the business service are automatically detected by comparing real-time metric data with corresponding historical metric data, in view of associated threshold values.

Claims36

Dependent claims

A method for monitoring a business service, the method comprising the steps of:

analyzing historical data for a first set of metrics, wherein each metric of said first set of metrics corresponds to one or more activities related to the performance of the business service;
based on said historical data, selecting a second set of metrics from the first set of metrics, wherein each metric in the second set of metrics is selected from the first set of metrics because historical data for the metric exhibits one or more repeating patterns;
and
from the second set of metrics, selecting one or more metrics to be one or more business rhythm metrics;
wherein said one or more business rhythm metrics are metrics that characterize normal behavior of an aspect of the business service;
wherein selecting the one or more business rhythm metrics comprises:
a) ranking each particular metric of the second set of metrics based on statistical characteristics of the one or more repeating patterns for the particular metric;
and b) selecting the one or more business rhythm metrics based on the ranking;
monitoring the one or more business rhythm metrics for variances in the normal behavior of the aspect of the business service, as represented by the one or more repeating patterns exhibited by the one or more business rhythm metrics;
in response to detecting a variance in the normal behavior of the aspect of the business service, as evidenced by values obtained for the one or more business rhythm metrics during the monitoring, triggering an action;
wherein the method is performed by one or more computing devices.

8 more elements

A method for monitoring a business service, the method comprising the steps of:

accessing historical data for a business activity metric, said historical data being partitioned into multiple data sets based on time ranges;
for each data set of the multiple data sets, determining a statistical distribution of the data set;
classifying at least a portion of the multiple data sets into groups of similarity, wherein, for each respective group of similarity, data sets are classified into the group of similarity because they have similar statistical distributions;
and
based on said classifying, storing business rhythm data representing a business rhythm for the business activity metric;
for each group of similarity, storing data that indicates the statistical distributions of the data in the group's data set;
monitoring the business activity metric for deviations from the business rhythm as indicated by said business rhythm data;
in response to detecting a deviation from the business rhythm, triggering an action;
monitoring real-time or near real-time data for said business activity metric for a particular time range;
classifying said data for said particular time range into a first group of similarity of said groups of similarity;
determining that the first group is not the same as a second group to which a data set for a corresponding similar time range was classified, as indicated by said business rhythm data;
in response to said step of determining, identifying a variance in the normal operational behavior of the business service;
wherein the method is performed by one or more computing devices.

10 more elements

One or more non-transitory storage media storing instructions which, when executed by one ore more computing devices, cause performance of:

analyzing historical data for a first set of metrics, wherein each metric of said first set of metrics corresponds to one or more activities related to the performance of a business service;
based on said historical data, selecting a second set of metrics from the first set of metrics, wherein each metric in the second set of metrics is selected from the first set of metrics because historical data for the metric exhibits one or more repeating patterns;
and
from the second set of metrics, selecting one or more metrics to be one or more business rhythm metrics;
wherein said one or more business rhythm metrics are metrics that characterize normal behavior of an aspect of the business service;
wherein selecting the one or more business rhythm metrics comprises:
a) ranking each particular metric of the second set of metrics based on statistical characteristics of the one or more repeating patterns for the particular metric;
and b) selecting the one or more business rhythm metrics based on the ranking;
monitoring the one or more business rhythm metrics for variances in the normal behavior of the aspect of the business service, as represented by the one or more repeating patterns exhibited by the one or more business rhythm metrics;
in response to detecting a variance in the normal behavior of the aspect of the business service, as evidenced by values obtained for the one or more business rhythm metrics during the monitoring, triggering an action.

7 more elements

One or more storage non-transitory media storing instructions, when executed by one or more computing devices, cause performance of:

accessing historical data for a business activity metric, said historical data being partitioned into multiple data sets based on time ranges;
for each data set of the multiple data sets, determining a statistical distribution of the data set;
classifying at least a portion of the multiple data sets into groups of similarity, wherein, for each respective group of similarity, data sets are classified into the group of similarity because they have similar statistical distributions;
and
based on said classifying, storing business rhythm data representing a business rhythm for the business activity metric;
for each group of similarity, storing data that indicates the statistical distributions of the data in the group's data set:
monitoring the business activity metric for deviations from the business rhythm as indicated by said business rhythm data;
in response to detecting a deviation from the business rhythm, triggering an action;
monitoring real-time or near real-time data for said business activity metric for a particular time range;
classifying said data for said particular time range into a first group of similarity of said groups of similarity;
determining that the first group is not the same as a second group to which a data set for a corresponding similar time range was classified, as indicated by said business rhythm data;
in response to said step of determining, identifying a variance in the normal operational behavior of the business service.

9 more elements

Dependencies are inferred from the claim text.