Cognitive Search Metadata Segmentation for DevOps
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Solution Overview
Problem
The DevOps process for cognitive search solutions is hindered by the need for maintaining duplicate size environments in development and production, leading to increased costs and performance issues as the document collection grows.
Innovation Solution
A method and system that optimizes cognitive searches by analyzing queries against an initial body of work using scorers to identify deployment-ready content, allowing for enhanced relevancy and minimizing the need for duplicate environments by using metadata to differentiate production-ready and development documents, and dynamically updating metadata based on performance evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If duplicate size environments are maintained for development and production, then exact copies of content and training data can be promoted, but costs increase and performance is adversely impacted
Solution Approach 1:
The patent segments the document collection by adding metadata fields to identify which documents are production-ready versus development or validation documents. This allows the system to logically separate environments without physically duplicating the entire document collection, thereby maintaining environment consistency while reducing the quantity of stored data.
Solution Approach 2:
Instead of maintaining exact duplicate copies of the entire document collection in both development and production environments, the patent uses metadata tags to indicate which documents are ready for deployment. This eliminates the need for physical copying of large datasets while ensuring that production receives the correct version of documents.
2Reliability
If duplicate size environments are maintained for development and production, then exact copies of content and training data can be promoted, but costs increase
Solution Approach 1:
The patent segments the document collection by adding metadata fields to identify which documents are production-ready versus development or validation documents. This allows the system to logically separate environments without physically duplicating the entire document collection, thereby maintaining environment consistency while reducing the quantity of stored data.
Solution Approach 2:
Instead of maintaining exact duplicate copies of the entire document collection in both development and production environments, the patent uses metadata tags to indicate which documents are ready for deployment. This eliminates the need for physical copying of large datasets while ensuring that production receives the correct version of documents.
3Reliability
If duplicate size environments are maintained for development and production, then exact copies of content and training data can be promoted, but performance is adversely impacted
Solution Approach 1:
The patent segments the document collection by adding metadata fields to identify which documents are production-ready versus development or validation documents. This allows the system to logically separate environments without physically duplicating the entire document collection, thereby maintaining environment consistency while reducing the quantity of stored data.
Solution Approach 2:
Instead of maintaining exact duplicate copies of the entire document collection in both development and production environments, the patent uses metadata tags to indicate which documents are ready for deployment. This eliminates the need for physical copying of large datasets while ensuring that production receives the correct version of documents.
Data Source
AI summary
A method and system are provided for implementing enhanced cognitive searches optimized to integrate deployment with development testing. An initial body of works is ingested into a system capable of answering questions. A series of queries is analyzed against the initial body of works utilizing a set of scorers utilizing criteria to form assessments, wherein each scorer uses the criteria against the query and the initial body of works to form the assessment. The assessments are analyzed to determine a usefulness of a set of entries in the initial body of works. Content are deployed using the identified selected first set of entries as deployment ready, enabling enhanced cognitive search results.