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US 11,153,176 B2Granted
Exponential moving maximum (EMM) filter for predictive analytics in network reporting
AssigneeInfoblox Inc.
InventorsBin Yu, Les Smith, Mark Threefoot
Life of this patent
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Abstract
Techniques for an exponential moving maximum (EMM) filter for predictive analytics in network reporting are disclosed. In some embodiments, a process for predictive analytics in network reporting using an EMM filter includes pre-processing network-related data by performing exponential moving maximum (EMM) filtering on the network-related data; and determining predictive analytics based on the EMM filtered network-related data.
Claims20
A system for predictive analytics in network reporting, comprising:
- a processor configured to:
- pre-process network-related data by performing exponential moving maximum (EMM) filtering on the network-related data, comprising to:
- aggregate a number of transactions per a period of time of network events over a plurality of periods of time to obtain the network-related data;
- and
- perform the EMM filtering on the network-related data, comprising to:
- identify a local maximum value from the network-related data based on a filtering window size, wherein the filtering window size relates to a period of time;
- and
- identify one or more bubble points based on one or more identified local maximum values over a range of time, wherein the range of time relates to one or more filtering window sizes, wherein a bubble point of the one or more bubble points over shadow other non-local maximum values, and wherein the one or more bubble points relates to the EMM filtered network-related data;
- and
- determine predictive analytics based on the EMM filtered network-related data, comprising to:
- generate a trend prediction based on the EMM filtered network-related data, comprising to:
- predict network bottlenecks in a network deployment using the identified local maximum value;
- and
- a memory coupled to the processor and configured to provide the processor with instructions.
11 more elements
A method of predictive analytics in network reporting, comprising:
- pre-processing network-related data by performing exponential moving maximum (EMM) filtering on the network-related data, comprising:
- aggregating a number of transactions per a period of time of network events over a plurality of periods of time to obtain the network-related data;
- and
- performing the EMM filtering on the network-related data, comprising:
- identifying a local maximum value from the network-related data based on a filtering window size, wherein the filtering window size relates to a period of time;
- and
- identifying one or more bubble points based on one or more identified local maximum values over a range of time, wherein the range of time relates to one or more filtering window sizes, wherein a bubble point of the one or more bubble points over shadow other non-local maximum values, and wherein the one or more bubble points relates to the EMM filtered network-related data;
- and
- determining predictive analytics based on the EMM filtered network-related data, comprising:
- generating a trend prediction based on the EMM filtered network-related data, comprising:
- predicting network bottlenecks in a network deployment using the identified local maximum value.
8 more elements
A computer program product for predictive analytics in network reporting, the computer program product being embodied in a tangible, non-transitory computer readable storage medium and comprising computer instructions for:
- pre-processing network-related data by performing exponential moving maximum (EMM) filtering on the network-related data, comprising:
- aggregating a number of transactions per a period of time of network events over a plurality of periods of time to obtain the network-related data;
- and
- performing the EMM filtering on the network-related data, comprising:
- identifying a local maximum value from the network-related data based on a filtering window size, wherein the filtering window size relates to a period of time;
- and
- identifying one or more bubble points based on one or more identified local maximum values over a range of time, wherein the range of time relates to one or more filtering window sizes, wherein a bubble point of the one or more bubble points over shadow other non-local maximum values, and wherein the one or more bubble points relates to the EMM filtered network-related data;
- and
- determining predictive analytics based on the EMM filtered network-related data, comprising:
- generating a trend prediction based on the EMM filtered network-related data, comprising:
- predicting network bottlenecks in a network deployment using the identified local maximum value.
8 more elements
Dependencies are inferred from the claim text.