18303509. CONFIGURING AN ENRICHED DATA METRICS PIPELINE simplified abstract (Oracle International Corporation)
Contents
- 1 CONFIGURING AN ENRICHED DATA METRICS PIPELINE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONFIGURING AN ENRICHED DATA METRICS PIPELINE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
CONFIGURING AN ENRICHED DATA METRICS PIPELINE
Organization Name
Oracle International Corporation
Inventor(s)
Swapnil Sinvhal of Belmont CA (US)
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.
