Distributed multi-data source performance management

• US 10,181,982 B2
• Filed: 02/09/2016
• Issued: 01/15/2019
• Est. Priority Date: 02/09/2015
• Status: Active Grant

• Current Assignee: Tupl, Inc.
• Original Assignee: Tupl, Inc.
• Inventors: Tapia, Pablo, Guerrero, Rafael
• Primary Examiner(s): Meky, Moustafa M
• Assistant Examiner(s): Waugh, Thorne E
• Application Number:
  US15/019,947
• Publication Number:
  US 20160248624A1
• Time in Patent Office: 1,071 Days
• CPC Class Codes: H04L 41/5009 Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/065 related to network devices
• CPC Class Codes: H04L 43/08 Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters

Abstract

A performance management engine may be implemented to continuously detecting entity performance issues. The performance management engine may calculate one or more key performance indicators (KPIs) that measure performance of an entity. The performance management engine may further receive one or more additional sources of data regarding the entity via a data management platform that interfaces with multiple data sources. The performance management engine may aggregate the KPIs and the one or more additional sources of data into datasets according to one or more grouping parameters. The data in one or more datasets may be analyzed by the performance management engine to generate one or more comprehensive performance indicators. The comprehensive performance indicators are then provided for display on a user device. Each KPI or comprehensive performance indicator measures performance of a device, a component, a node, or a service of the entity.

Claims

1. One or more non-transitory computer-readable media storing computer-executable instructions that upon execution cause one or more processors to perform acts comprising:

calculating one or more key performance indicators (KPIs) for an entity, the calculating the one or more KPIs including;

receiving operation data regarding operations of the entity via the data management platform,

aggregating the operation data into a plurality of datasets according to one or more grouping parameters,

selecting one or more predicted KPIs for the entity based on historical query request information, the one or more predicted KPIs being likely to be requested in an upcoming time period, and

pre-calculating the one or more predicted KPIs for the entity according to the operation data in one or more datasets of the plurality of datasets,

receiving one or more additional sources of data regarding the entity via a data management platform that interfaces with multiple data sources;

aggregating the one or more KPIs and the one or more additional sources of data into datasets according to one or more grouping parameters;

analyzing data in one or more datasets to generate one or more comprehensive performance indicators; and

providing at least one comprehensive performance indicator for display on a user device, wherein each KPI and each comprehensive performance indicator respectively indicates a performance of a device, a component, a node, or a service of the entity.

2. The one or more non-transitory computer-readable media of claim 1, wherein the one or more additional sources of data includes a social media data source, an alarm data source of the entity, a trouble ticket data source of the entity, or a tool data source of the entity.

3. The one or more non-transitory computer-readable media of claim 1, wherein a comprehensive performance indicator specifies a performance issue associated with the entity, and wherein the acts further comprise:

generating one or more alerts associated with the comprehensive performance indicator; and

providing the one or more alerts for presentation on a user device, the one or more alerts providing at least one solution for remedying the performance issue with the entity.

4. The one or more non-transitory computer-readable media of claim 1, wherein the calculating the one or more KPIs further includes:

determining whether a specific KPI that is requested corresponds to a pre-calculated predicted KPI, the specific KPI being requested for fulfilling a performance data query that is initiated at a user device or generating a comprehensive performance indicator,

retrieving the pre-calculated predicted KPI from a KPI cache for presentation on the user device or generation of the comprehensive performance indicator in response to a determination that the specific KPI corresponds to the pre-calculated predicted KPI, and

calculating the specific KPI for the entity according to the operation data in a corresponding dataset of the plurality of datasets, and providing the specific KPI for presentation on the user device or the generation of the comprehensive performance indicator, in response to a determination that the one or more predicted KPIs as pre-calculated fail to correspond to the specific KPI.

5. The one or more non-transitory computer-readable media of claim 4, wherein the performance data query is from a particular party, and the one or more KPIs are pre-calculated for a prior performance data query from a previous party that differs from the particular party.

6. The one or more non-transitory computer-readable media of claim 4, wherein the retrieving or providing for presentation includes send a KPI to the user device for display in a geographical view or a chart view.

7. The one or more non-transitory computer-readable media of claim 1, where the acts further comprise:

receiving operation data regarding operations of the entity via the data management platform;

aggregating the operation data into a plurality of datasets according to one or more grouping parameters;

calculating multiple KPI trends for the entity according to the operation data in the datasets;

receiving an anomaly query for the entity that specifies a geographical location and a time period without specifying one or more KPIs to be retrieved; and

providing one or more KPI trends that show at least one performance anomaly of the entity in response to the anomaly query.

8. The one or more non-transitory computer-readable media of claim 7, wherein the acts further comprise:

receiving a request to view a KPI trend associated with an alert, the KPI trend indicating a performance anomaly of the entity; and

providing the requested KPI trend for presentation on a user device.

9. The one or more non-transitory computer-readable media of claim 1, wherein the one or more grouping parameters include a specific time period, a specific entity component, a specific user device vendor, a specific user device model, or different levels of an entity hierarchy that includes a subscriber level, a device level, a service area level, and a geographical market level, wherein the geographical level further includes a zip code sublevel, a municipality sublevel, or an additional location-based level.

10. The one or more non-transitory computer-readable media of claim 1, wherein the entity is a wireless communication carrier that delivers telecommunication service to subscribers or a utility system that delivers a utility service to customers.

11. The one or more non-transitory computer-readable media of claim 1, wherein the calculating includes calculating the one or more KPIs continuously on a periodic basis for new time intervals based on new operation data from an operation data source of the entity, and the analyzing includes continuously analyzing or analyzing on a periodic basis using newly calculated KPIs and new data from the one or more data sources.

12. The one or more non-transitory computer-readable media of claim 1, further comprising increasing a number of computing nodes that perform the calculating one or more KPIs or the analyzing data in the one or more datasets in response to an increase in performance data processing load, or decreasing the number of computing nodes that perform the calculating one or more KPIs or the analyzing data in the one or more datasets in response to a decrease in performance data processing load.

13. A computer-implemented method, comprising:

calculating one or more key performance indicators (KPIs) for an entity via a performance management engine that executes on one or more computing nodes, the calculating the one or more KPIs including;

receiving operation data regarding operations of the entity via the data management platform;

aggregating the operation data into a plurality of datasets according to one or more grouping parameters;

selecting one or more predicted KPIs for the entity based on historical query request information, the one or more predicted KPIs being likely to be requested in an upcoming time period, and

pre-calculating the one or more predicted KPIs for the entity according to the operation data in one or more datasets of the plurality of datasets;

receiving one or more additional sources of data regarding the entity via a data management platform that interfaces with multiple data sources, the multiple data sources further including one or more of a social media data source, an alarm data source of the entity, a trouble ticket data source of the entity, or a tool data source of the entity;

aggregating the one or more KPIs and the one or more additional sources of data into datasets according to one or more grouping parameters, the one or more grouping parameters include a specific time period, a specific entity component, a specific user device vendor, a specific user device model, or different levels of an entity hierarchy that includes a subscriber level, a device level, a service area level, and a geographical market level;

analyzing data in one or more datasets to generate one or more comprehensive performance indicators via the performance management engine; and

providing at least one comprehensive performance indicator for display on a user device, wherein each KPI and each comprehensive performance indicator respectively indicates a performance of a device, a component, a node, or a service of the entity.

15. The computer-implemented method of claim 13, further comprising:

receiving operation data regarding operations of the entity via the data management platform that interfaces with multiple data sources;

aggregating the operation data into a plurality of datasets according to one or more grouping parameters;

calculating multiple KPI trends for the entity according to the operation data in the datasets;

generating at least one alert for one or more KPI trends that indicate at least one performance anomaly of the entity; and

providing the at least one alert for the one or more KPI trends that indicate at least one performance anomaly of the entity for display.

16. The computer-implemented method of claim 13, further comprising:

receiving a request to view a KPI trend associated with an alert, the KPI trend indicating a performance anomaly of the entity; and

providing the requested KPI trend for presentation on a user device.

17. The computer-implemented method of claim 13, wherein a KPI or a comprehensive performance indicator of the one or more comprehensive performance indicators is to be provided for display by a dashboard application on the user device by filtering according to a specific time period, a specific view, a specific component of the entity, a specific node of the entity, or a specific service of the entity.

18. The computer-implemented method of claim 13, wherein the one or more sources include structured databases that are accessible to data-agnostic data adapters of the data management platform.

14. The computer-implemented method of 13, wherein the calculating the one or more KPIs further includes:

receiving operation data regarding operations of the entity via the data management platform,

aggregating the operation data into a plurality of datasets according to one or more grouping parameters,

pre-calculating the one or more KPIs for the entity according to the operation data in one or more datasets of the plurality of datasets,

determining whether a specific KPI that is requested corresponds to a pre-calculated predicted KPI, the specific KPI being requested for fulfilling a performance data query that is initiated at a user device or generating a comprehensive performance indicator,

retrieving the pre-calculated predicted KPI from a KPI cache for presentation on the user device or generation of the comprehensive performance indicator in response to a determination that the specific KPI corresponds to the pre-calculated predicted KPI, and

calculating the specific KPI for the entity according to the operation data in a corresponding dataset of the plurality of datasets, and providing the specific KPI for presentation on the user device or the generation of the comprehensive performance indicator, in response to a determination that the one or more predicted KPIs as pre-calculated fail to correspond to the specific KPI.

19. A system, comprising:

one or more processors; and

memory including a plurality of computer-executable components that are executable by the one or more processors to perform a plurality of actions, the plurality of actions comprising;

receiving operation data regarding operations of an entity via a data management platform that interfaces with multiple data sources, the multiple data sources including an operation data source that stores the operation data;

aggregating the operation data into a plurality of datasets according to one or more grouping parameters, the one or more grouping parameters include a specific time period, a specific entity component, a specific user device vendor, a specific user device model, or different levels of an entity hierarchy that includes a subscriber level, a device level, a service area level, and a geographical market level; and

selecting one or more predicted Key Performance Indicators (KPIs) for the entity based on historical query request information, the one or more predicted KPIs being likely to be requested in an upcoming time period,

pre-calculating the one or more predicated KIPIs for the entity according to the operation data in one or more datasets of the plurality of datasets;

determining whether a specific KPI that is requested corresponds to a pre-calculated predicated KPI;

retrieving the pre-calculated KPI from a KPI cache in response to a determination that the specific KPI corresponds to the pre-calculated predicated KPI; and

calculating the specific KPI for the entity according to the operation data in a corresponding dataset of the plurality of datasets in response to a determination that the one or more predicated KPIs as pre-calculated fail to correspond to the specific KPI.

20. The system of claim 19, wherein the plurality of actions further comprises:

receiving one or more additional sources of data regarding the entity via the data management platform that interfaces with the multiple data sources, the multiple data sources further including one or more of a social media data source, an alarm data source of the entity, or a trouble ticket data source of the entity, or a tool data source of the entity;

aggregating the specific KPI and the one or more additional sources of data into datasets according to one or more grouping parameters;

analyzing data in one or more datasets to generate one or more comprehensive performance indicators; and

providing at least one comprehensive performance indicator for display on a user device, wherein each KPI and each comprehensive performance indicator respectively indicates a performance of a device, a component, a node, or a service of the entity.