AI Search System for Mainframe Dataset Context
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Solution Overview
Problem
Existing mainframe systems face challenges in efficiently performing searches within datasets due to the complexity of identifying context for queried keywords, which is crucial for resolving production problems.
Innovation Solution
A method and system utilizing an AI-based model to identify and tag relevant steps and paragraphs containing keywords within a mainframe dataset, followed by verification and display of the results, enhancing search efficiency and context provision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search functions are used in mainframe systems, then the search operation is simple to implement, but the search cannot provide context for queried keywords and is tedious and complicated
Solution Approach 1:
The patent introduces an AI-based model as an intermediary between the user's keyword query and the dataset. This intermediary automatically identifies relevant steps and paragraphs, extracts context information, and presents it in a structured format, thereby bridging the gap between simple search operations and comprehensive context provision.
Solution Approach 2:
The patent replaces conventional mechanical search functions with an AI-based automated system. The AI model processes queries, identifies relevant data, extracts context, and formats results automatically, eliminating the need for manual, tedious search operations while providing comprehensive context information.
2Reliability
If AI-based model is used to identify and tag relevant steps and paragraphs, then search reliability and context provision are improved, but system complexity increases
Solution Approach 1:
The patent segments the search process into distinct phases: query reception, AI-based identification of relevant steps and paragraphs, tagging with identifiers, verification, and display. This segmentation allows the complex AI-based system to handle complexity systematically while maintaining high reliability through structured processing.
Solution Approach 2:
The AI-based model performs self-service by automatically identifying relevant data, extracting context information, and formatting results without requiring manual intervention. This automation maintains high reliability while managing system complexity through intelligent self-processing capabilities.
3Productivity
If comprehensive context is extracted for each keyword, then problem resolution is expedited, but processing time and resource consumption increase
Solution Approach 1:
The patent applies partial action by using AI-based identification to extract only the necessary context information related to queried keywords, rather than processing the entire dataset. This selective extraction expedites problem resolution by focusing resources on relevant information while minimizing unnecessary processing time.
Solution Approach 2:
The system performs preliminary action by pre-processing and indexing the dataset to enable faster AI-based identification during actual search operations. This preparation allows rapid extraction of relevant context information when queries are received, reducing processing time while maintaining comprehensive problem resolution capability.
Data Source
AI summary
A method and a system for performing a search in a dataset in a mainframe session are disclosed. The method includes receiving at least one keyword associated with at least one query. The method includes identifying at least one from among at least one step containing the at least one keyword and at least one paragraph containing the at least one keyword based on the at least one query. The method includes tagging at least one identifier to at least one from among the at least one identified step and the at least one identified paragraph. Next, the method includes verifying the at least one tagged identifier. Thereafter, the method includes displaying at least one from among the at least one identified step and the at least one identified paragraph.