Multi-Cloud Query Generation via AI Parsing
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Solution Overview
Problem
Existing data extraction applications across multi-cloud storage systems and databases face challenges in intuitive query generation, efficient data extraction, and visually appealing results.
Innovation Solution
A method and system that automatically generate a sequence of queries based on a user query, using metadata, sample data, or data source references from attributed data sources, and employing a text generation model to produce SQL or SQL-like queries for comprehensive data retrieval and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional user query and data extraction applications are used, then basic data retrieval is possible, but query generation is not intuitive and data extraction is inefficient
Solution Approach 1:
The system automatically generates queries and extracts data without requiring users to manually construct complex queries. The query generation module creates appropriate queries based on user selections, and the data extraction module automatically retrieves and processes data from multiple cloud storage systems, eliminating the need for users to manually navigate and query dispersed data sources.
Solution Approach 2:
The system introduces an intermediary layer between the user and the dispersed data sources. The query generation module and data extraction module act as intermediaries that translate user intentions into efficient queries and automatically handle the complex task of retrieving data from multiple cloud storage systems, thereby improving both ease of operation and productivity.
2Adaptability or versatility
If data is stored across various storage systems and databases within a cloud environment, then data availability is improved, but retrieval and analysis becomes challenging
Solution Approach 1:
The system implements a universal data extraction module that can retrieve data from multiple different cloud storage systems and databases using a single unified interface. This multi-functional approach allows the system to adapt to various storage locations while presenting a consistent, simplified interaction model, thereby maintaining storage flexibility while reducing retrieval and analysis complexity.
3Reliability
If manual data extraction and query formulation is required, then control over data retrieval is maintained, but time and resources are consumed
Solution Approach 1:
The system performs preliminary actions by automatically generating optimized queries and preparing data extraction parameters before execution. The query generation module pre-formulates appropriate queries based on user selections and data schema information, and the data extraction module pre-configures retrieval parameters, thereby maintaining reliable control over data retrieval while significantly reducing the time required for actual query processing.
Data Source
AI summary
A method and system for handling a user query are provided. The method includes setting up a symbol or link in a user interface, the symbol or link representing a dataset in a cloud environment, receiving a user query initiated by a user through the user interface, parsing the dataset to identify a data file associated with the user query, generating a sequence of queries based on the user query and content parsed from the data file, and searching against the data file using the sequence of queries to obtain a set of query results.