Context-Driven Query Engine for Dynamic SQL and KPI Visualization
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Solution Overview
Problem
Conventional analytical query engines fail to understand user context, generate dynamic SQL queries based on user behavior, and provide intelligent visualization, limiting their ability to fetch and combine data from diverse data sources effectively.
Innovation Solution
A processor-based method that analyzes user queries using metadata from a database management system to determine context, generate user metadata, identify key performance indicators, and create intelligent visual representations, dynamically building SQL queries to retrieve relevant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional analytical query engines use pre-programmed applications with stored SQL queries, then they can provide output for queries within the pre-programmed scope, but they fail to provide output when user queries are beyond the purview of linked data sources and cannot understand user context
Solution Approach 1:
The system enables self-service by automatically generating SQL queries based on user input and context analysis without requiring pre-programmed query templates. The analytical query engine autonomously determines which data sources to access and constructs appropriate queries, eliminating the need for users to manually write SQL while maintaining reliable output generation
Solution Approach 2:
The system dynamically changes operational parameters by adjusting query generation strategies based on analyzed user context, behavior patterns, and data source metadata. This allows the engine to adapt its query construction approach for different user needs and query types, enhancing both reliability and versatility simultaneously
2Device complexity
If conventional analytical query engines rely on pre-programmed applications, then the system complexity is reduced, but the extent of automation in generating relevant queries based on user behavior and context is limited
Solution Approach 1:
The system performs preliminary actions by pre-analyzing user behavior patterns, query history, and data source metadata before actual query execution. This preliminary context analysis and user profiling enables the automated query generation to be more accurate and relevant, achieving high automation without excessive complexity
Solution Approach 2:
The system introduces an intermediary layer between user input and SQL query generation that automatically analyzes context, user persona, and data source characteristics. This intermediary processing layer enables sophisticated automated query generation while maintaining manageable system complexity through modular architecture
3Ease of operation
If conventional analytical query engines use simple query processing, then the ease of operation is improved, but the ability to provide intelligent visualization and context-based analysis is compromised
Solution Approach 1:
The system adds another dimension by implementing multi-level context analysis that examines user behavior, query patterns, data source metadata, and business logic simultaneously. This dimensional expansion of context processing enables intelligent visualization and comprehensive analysis while maintaining simple natural language query input for users
Data Source
AI summary
A method for performing context-based analysis using a processor is disclosed. The method is initialized when the processor receives a first query from a first user on a first user device. Further, processor is configured to send the first query from the first user device using metadata from a database management system over a network. The sent first query is analyzed. The processor is configured to analyze the first query and determine a first context of the first query to generate a first user metadata based on the first context. Further, the processor is configured to identify the key performance indicators (KPIs) in the first user metadata using an intelligent visualization configuration. The processor is configured to use the intelligent visualization configuration to generate a first visual representation of the identified KPIs.