Self-learning integrity management system and related methods
First Claim
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1. A dynamic thresholding system comprising, in combination:
- a computer in data communication with an information technology infrastructure, the computer having;
a data collection module that collects metric data and stores the metric data on a storage device from at least one metric in the information technology infrastructure; and
a dynamic threshold generator for creating a dynamic threshold based on a set of historical metric data, comprising;
a model execution engine for analyzing the historical metric data based on at least one analytics model in an analytics model library;
an analytics model library rule that defines parameters used by the model execution engine to form a transformed metric data set; and
a trend detecting module for detecting a trend, the trend occurring when incoming metric data substantially exceeds the dynamic threshold for a predetermined number of time slices;
wherein the dynamic threshold generated from the dynamic threshold generator is not static from one time slice to another time slice; and
wherein a resolution module causes an alarm when a trend is detected.
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Abstract
An integrity management system predicts abnormalities in complex systems before they occur based upon the prior history of abnormalities within the complex system. A topology of the nodes of a complex system is generated and data is collected from the system based on predetermined metrics. In combination with dynamic thresholding, fingerprints of the relevant nodes within a complex system at various time intervals prior to the occurrence of the abnormality are captured and weighted. The fingerprints can then be applied to real-time data to provide alerts of potential abnormality prior to their actual occurrence.
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Citations
17 Claims
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1. A dynamic thresholding system comprising, in combination:
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a computer in data communication with an information technology infrastructure, the computer having; a data collection module that collects metric data and stores the metric data on a storage device from at least one metric in the information technology infrastructure; and a dynamic threshold generator for creating a dynamic threshold based on a set of historical metric data, comprising; a model execution engine for analyzing the historical metric data based on at least one analytics model in an analytics model library; an analytics model library rule that defines parameters used by the model execution engine to form a transformed metric data set; and a trend detecting module for detecting a trend, the trend occurring when incoming metric data substantially exceeds the dynamic threshold for a predetermined number of time slices; wherein the dynamic threshold generated from the dynamic threshold generator is not static from one time slice to another time slice; and wherein a resolution module causes an alarm when a trend is detected. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A non-transitory machine-readable medium having instructions stored thereon executable by a processing unit comprising:
using a computer to; collect data from at least one metric in an information technology infrastructure and store the data; transform the data with at least one model from an analytics model library and at least one model library rule and storing the transformed data in a set of historical transformed data; determine a dynamic threshold for the at least one metric on a time slice by time slice basis by using the set of historical transformed data to generate a value describing a limit of normal functionality for each time slice; and modify the dynamic threshold periodically to reflect recent historical transformed data by replacing old data with more recent historical data in the set of historical transformed data. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14)
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15. A non-transitory machine-readable medium having instructions stored thereon executable by a processing unit comprising:
using a computer to; collect data from at least one metric in an information technology infrastructure and store the data; transform the data with at least one model from an analytics model library and at least one model library rule and storing the transformed data in a set of historical transformed data; determine a dynamic threshold for the at least one metric on a time slice by time slice basis by using the set of historical transformed data to generate a value describing a limit of normal functionality for each time slice; modify the dynamic threshold periodically to reflect recent historical transformed data by replacing old data with more recent historical data in the set of historical transformed data; comparing incoming metric data against the dynamic threshold for the corresponding time slice in which the incoming metric data is collected; and triggering an alert state when a trend is observed, wherein the trend comprises the value of the incoming metric data exceeding the dynamic threshold for a predetermined period of time. - View Dependent Claims (16, 17)
Specification