Cloud Environment Provisioning Through Text-Mined Feature Matching
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Solution Overview
Problem
Small businesses and organizations lack the necessary IT knowledge to migrate to appropriate cloud platforms (IaaS, PaaS, or SaaS) due to the complexity of cloud service features such as real-time availability, streaming, user experience, and scalable data storage.
Innovation Solution
A computer-implemented method that receives unstructured text from users describing items for migration to a cloud environment, performs text mining to extract item features, identifies entities, maps these to available cloud features, and automatically recommends suitable cloud features to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud service features and configurations are made more comprehensive and detailed, then the ability to meet diverse business needs is improved, but the complexity of understanding and selecting appropriate cloud platforms increases
Solution Approach 1:
The patent introduces an intermediary system that includes a text processing module and a recommendation module. This intermediary automatically analyzes user requirements, extracts key features, and maps them to appropriate cloud platform configurations, thereby resolving the contradiction between comprehensive cloud service adaptability and the complexity of selecting and understanding cloud platforms.
Solution Approach 2:
The system enables self-service by automatically generating cloud platform recommendations based on user-provided requirements. The text processing module extracts features from user input, and the recommendation module autonomously matches these features with suitable cloud configurations, eliminating the need for users to manually navigate complex cloud platform options.
2Ease of operation
If automated recommendation systems are implemented, then the ease of operation for users is improved, but the extent of automation increases system complexity
Solution Approach 1:
The automated recommendation system is segmented into distinct functional modules: a text processing module for extracting requirements and features, and a recommendation module for generating cloud platform suggestions. This segmentation manages system complexity by organizing automation functions into manageable, independent components while maintaining ease of operation for users.
Data Source
AI summary
Text is received from a user describing item(s) for migration to a computing environment with cloud feature(s), resulting in item description(s), the text including unstructured text that are processed separately. Text mining is performed on the unstructured text to extract item feature(s). For each listing a portion of the unstructured text is extracted, resulting in an extracted text portion for each listing from which an entity is identified. Each entity or item feature is mapped to cloud feature(s) available from solution(s) with cloud feature(s). Based on the cloud feature(s), recommendation(s) are made to the user regarding cloud feature(s) of the solution(s) for optional consideration by the user. Explanation(s) for the recommended cloud feature(s) from explainability model(s) may be provided to the user.