AI NLP Middleware Data Collection
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Solution Overview
Problem
Current Message Oriented Middleware (MOM) systems require administrators to write and execute different scripts and commands for various MOM vendors, leading to time-consuming procedural tasks and increased complexity in gathering data and performing health checks across heterogeneous environments.
Innovation Solution
The implementation of artificial intelligence (AI) and natural language processing (NLP) enables users to enter natural language queries, which are processed to retrieve data from multiple MOM vendors without requiring specific scripts or commands for each provider, using a platform that analyzes and executes stored queries in native command formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If administrators use conventional approaches to collect operational data from messaging systems using multiple MOM vendors, then data can be gathered from each vendor, but administrators must write and execute different scripts and commands in languages native to each MOM provider, leading to time-consuming procedural tasks
Solution Approach 1:
The patent introduces an intermediary layer (the system with processors and storage devices) that mediates between the administrator and multiple MOM vendors. This intermediary translates natural language queries into vendor-specific commands, eliminating the need for administrators to learn multiple proprietary languages and significantly reducing the time required for data collection across heterogeneous MOM environments.
2Adaptability or versatility
If administrators rely on proprietary protocols and commands of respective MOM platforms to perform health checks and gather usage data, then data can be retrieved from each platform, but the complexity associated with interfacing with different MOM products increases
Solution Approach 1:
The patent implements a universal interface that can interact with multiple different MOM vendors through a single standardized method. The system stores and executes multiple vendor-specific commands in a unified manner, allowing administrators to perform health checks and data collection across heterogeneous MOM environments without needing to understand or manage the complexity of individual proprietary protocols.
3Reliability
If different data retrieval scripts and commands are used for each MOM provider, then vendor-specific data can be obtained, but the administrative complexity and procedural overhead increase significantly
Solution Approach 1:
The patent creates a standardized template or copy of the data collection process that can be applied uniformly across different MOM vendors. Instead of requiring administrators to manually create and maintain separate scripts for each vendor, the system stores vendor-specific command templates and automatically selects and executes the appropriate template based on the target MOM provider, maintaining data retrieval accuracy while dramatically simplifying the operator's task.
Data Source
AI summary
A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more stored queries corresponding to the determined one or more types of the data are identified. The one or more stored queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the identified one or more stored queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.