18542282. Automatically-Generated Labels For Time Series Data And Numerical Lists To Use In Analytic And Machine Learning Systems simplified abstract (Oracle International Corporation)

Automatically-Generated Labels For Time Series Data And Numerical Lists To Use In Analytic And Machine Learning Systems

Organization Name

Oracle International Corporation

Inventor(s)

Amr Fawzy Fahmy of Foxboro MA (US)

Sreeji Krishnan Das of Fremont CA (US)

Adrienne Wong of Redwood City CA (US)

Jae Young Yoon of San Mateo CA (US)

Dhileeban Kumaresan of Foster City CA (US)

Eric L. Sutton of Naperville IL (US)

Automatically-Generated Labels For Time Series Data And Numerical Lists To Use In Analytic And Machine Learning Systems - A simplified explanation of the abstract

This abstract first appeared for US patent application 18542282 titled 'Automatically-Generated Labels For Time Series Data And Numerical Lists To Use In Analytic And Machine Learning Systems

Simplified Explanation

The patent application describes techniques for performing analytics using automatically generated labels for time series data and numerical lists.

• The system loads one or more time series datasets with data points based on varying values of a metric over time.
• Labels are assigned to a subset of data points in the datasets, describing patterns relative to other data points.
• The system identifies patterns of automatically assigned labels indicative of events affecting computing resources.
• Responsive actions are triggered based on the identified patterns.

Potential Applications

This technology could be applied in various fields such as:

• Predictive maintenance in manufacturing industries
• Anomaly detection in network traffic analysis
• Performance monitoring in cloud computing environments

Problems Solved

This technology helps in:

• Automating the process of identifying patterns in time series data
• Improving the efficiency of event detection in computing resources
• Enhancing the accuracy of analytics by using automatically generated labels

Benefits

The benefits of this technology include:

• Faster detection of events affecting computing resources
• Improved decision-making based on identified patterns
• Increased efficiency in analyzing large datasets

Potential Commercial Applications

Potential commercial applications of this technology could include:

• Software tools for real-time monitoring and alerting
• Consulting services for implementing analytics solutions
• Integration with existing data analysis platforms for enhanced insights

Possible Prior Art

One possible prior art for this technology could be the use of machine learning algorithms for anomaly detection in time series data.

How does the system assign labels to data points in the time series datasets?

The patent application does not provide specific details on the methodology or algorithm used for assigning labels to data points.

What types of responsive actions can be triggered based on the identified patterns of labels?

The patent application does not elaborate on the specific actions that can be triggered in response to the identified patterns of labels.

Original Abstract Submitted

Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.