View source for 18303509. CONFIGURING AN ENRICHED DATA METRICS PIPELINE simplified abstract (Oracle International Corporation)

=CONFIGURING AN ENRICHED DATA METRICS PIPELINE=


==Organization Name==

[[:Category:Oracle International Corporation|Oracle International Corporation]]


[[Category:Oracle International Corporation]]

==Inventor(s)==

[[:Category:Swapnil Sinvhal of Belmont CA (US)|Swapnil Sinvhal of Belmont CA (US)]][[Category:Swapnil Sinvhal of Belmont CA (US)]]

[[:Category:Elizabeth Li of Sugar Land TX (US)|Elizabeth Li of Sugar Land TX (US)]][[Category:Elizabeth Li of Sugar Land TX (US)]]

==CONFIGURING AN ENRICHED DATA METRICS PIPELINE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18303509 titled 'CONFIGURING AN ENRICHED DATA METRICS PIPELINE

==Simplified Explanation==

The abstract describes techniques for configuring an enriched data metrics pipeline (DMP) for nodes of a heterogeneous computing platform. The process involves obtaining node configuration data associated with an unenriched DMP, configuring the enriched DMP based on this data to provide node-level enriched data to a service, and generating fleet-level data metrics based on the node-level enriched data.

* Obtaining node configuration data for different kinds of nodes in a computing platform
* Configuring an enriched DMP to provide node-level enriched data to a service
* Generating fleet-level data metrics based on the node-level enriched data

== Potential Applications ==
The technology described in the patent application could be applied in various industries where data metrics are crucial for performance monitoring and optimization. Some potential applications include:
* Data analytics
* Internet of Things (IoT) devices
* Cloud computing

== Problems Solved ==
The technology addresses the following problems:
* Efficiently obtaining and processing data from heterogeneous computing platforms
* Generating accurate and timely data metrics for performance analysis
* Optimizing data processing schedules for improved resource utilization

== Benefits ==
The benefits of this technology include:
* Improved performance monitoring and optimization
* Enhanced data analysis capabilities
* Streamlined data processing workflows

== Potential Commercial Applications ==
The technology has potential commercial applications in industries such as:
* Data analytics software development
* Cloud service providers
* IoT device manufacturers

== Possible Prior Art ==
One possible prior art for this technology could be existing data processing systems that handle node-level data metrics in computing platforms. These systems may have similar functionalities but may not specifically address the configuration of enriched data metrics pipelines for heterogeneous platforms.

== Unanswered Questions ==
=== How does the technology handle data security and privacy concerns? ===
The article does not mention how the technology ensures the security and privacy of the data being processed and analyzed.

=== What are the scalability limitations of the technology? ===
The scalability of the technology in handling large volumes of data and increasing numbers of nodes in a computing platform is not discussed in the article.


==Original Abstract Submitted==

Techniques for configuring an enriched data metrics pipeline (DMP) include: obtaining node configuration data associated with an unenriched DMP for nodes of a heterogeneous computing platform, including (a) a first kind of node including an application programming interface (API) for obtaining unenriched data associated with the first kind of node and (b) a second kind of node including an API for obtaining unenriched data associated with the second kind of node; the unenriched DMP being configured to provide node-level unenriched data to a service according to a first schedule; the service being configured to generate node-level data metrics based on the unenriched data; based on the node configuration data, configuring an enriched DMP to provide node-level enriched data to the service according to a second schedule that is less frequent than the first schedule; the service being configured to generate fleet-level data metrics based on the node-level enriched data.



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