Dell products l.p. (20240104051). SYSTEM AND METHOD FOR ANALYZING FILE SYSTEM DATA USING A MACHINE LEARNING MODEL APPLIED TO A METADATA BACKUP simplified abstract
Contents
- 1 SYSTEM AND METHOD FOR ANALYZING FILE SYSTEM DATA USING A MACHINE LEARNING MODEL APPLIED TO A METADATA BACKUP
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR ANALYZING FILE SYSTEM DATA USING A MACHINE LEARNING MODEL APPLIED TO A METADATA BACKUP - 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 Original Abstract Submitted
SYSTEM AND METHOD FOR ANALYZING FILE SYSTEM DATA USING A MACHINE LEARNING MODEL APPLIED TO A METADATA BACKUP
Organization Name
Inventor(s)
Shelesh Chopra of Bangalore (IN)
SYSTEM AND METHOD FOR ANALYZING FILE SYSTEM DATA USING A MACHINE LEARNING MODEL APPLIED TO A METADATA BACKUP - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104051 titled 'SYSTEM AND METHOD FOR ANALYZING FILE SYSTEM DATA USING A MACHINE LEARNING MODEL APPLIED TO A METADATA BACKUP
Simplified Explanation
The abstract describes a method for managing data by analyzing a file system using an analytics engine operating on an external environment. The method involves obtaining a file system analysis request, obtaining a metadata backup associated with the file system, applying a machine learning model on the metadata backup to extract attributes, performing a metadata analysis using the extracted attributes, and providing the processed result to the application.
- Obtaining file system analysis request from an application and analytics engine
- Obtaining metadata backup associated with the file system
- Applying machine learning model to extract attributes from the metadata backup
- Performing metadata analysis using the extracted attributes
- Providing the processed result to the application
Potential Applications
The technology described in the patent application could be applied in various industries such as data management, analytics, and information technology.
Problems Solved
This technology helps in efficiently managing and analyzing file system data, extracting relevant attributes, and providing valuable insights to applications.
Benefits
The benefits of this technology include improved data analysis, enhanced decision-making capabilities, and optimized file system management.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of data analytics software for businesses looking to streamline their data management processes.
Possible Prior Art
One possible prior art could be the use of machine learning models for data analysis and extraction in the field of information technology.
Unanswered Questions
How does this technology ensure data security and privacy during the analysis process?
The technology described in the abstract focuses on data analysis and extraction, but it does not explicitly mention how data security and privacy are maintained during the process.
What are the scalability limitations of this method when analyzing large file systems?
The abstract does not address the potential scalability limitations of the method when dealing with large file systems and massive amounts of data.
Original Abstract Submitted
a method for managing data includes obtaining, by an analytics engine and from an application, a file system analysis request for analyzing a file system, wherein the analytics engine is operating on a second environment that is external to a client environment, wherein the file system comprises an organization of file system data, wherein the file system data is generated by the application, in response to the file system analysis request: obtaining a metadata backup, wherein the metadata backup is associated with the file system, applying a machine learning model on the metadata backup to extract a portion of attributes, performing a metadata analysis using the portion of attributes to obtain a processed result, and providing the processed result to the application.
