POSIX Context Extraction Module for Data Access Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current POSIX-compliant storage systems do not obtain and store contextual information about data access operations, which is useful for analytical purposes such as trend analysis and optimizing data access efficiency.
Innovation Solution
A context extraction module intercepts data calls made through POSIX APIs, obtaining contextual information like process names, call stack states, and access types, and stores this information as metadata or in a database for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If contextual information is collected and stored for each data access operation, then analytical capabilities and system optimization potential are improved, but system complexity and overhead increase
Solution Approach 1:
The patent introduces a context extraction module as an intermediary component that sits between the POSIX API and the kernel. This module intercepts data access calls, extracts contextual information (such as process IDs, file paths, access types), and stores this information in a separate context database. By using this intermediary approach, the system captures comprehensive contextual information without fundamentally altering the core POSIX file access mechanism, thus improving information collection while managing system complexity through modular design
2Loss of information
If contextual information is stored for every data access call, then data access trend analysis is improved, but storage overhead and processing time increase
Solution Approach 1:
The patent extracts only the essential contextual information from data access calls and stores it in a dedicated context database, separating this metadata from the actual data access operations. The context extraction module identifies and extracts key elements such as process identifiers, file paths, access types (read/write), and timestamps, while leaving the main data flow unchanged. This extraction approach enables comprehensive trend analysis without requiring storage or processing of the entire data access workload
Solution Approach 2:
The system performs preliminary extraction and storage of contextual information at the point of data access, rather than analyzing complete data sets later. By capturing contextual metadata upfront during the data access call, the system prepares analysis-ready data in advance, enabling efficient trend analysis and system optimization without the need for time-consuming post-processing of raw data access logs
Data Source
AI summary
A method, performed by a computing system, includes detecting a data call from an application running on the system, the data call being to access data through a Portable Operating System Interface (POSIX) Application Programming Interface (API), the data being stored on a storage system associated with the computing system. The method further includes, in response to detecting the data call, obtaining, through use of a context extraction module, contextual information associated with the data call. The method further includes accessing the data according to the data call and storing the contextual information.