AI Query Mapping for Disparate Data Source Optimization
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Solution Overview
Problem
Existing systems face inefficiencies in generating queries for disparate data sources in data lakes, leading to excessive computing resource usage and inability to reuse derived attributes across applications.
Innovation Solution
A system that identifies queries, determines attributes and intents, generates maps and heat maps to optimize queries based on computer processing consumption, using AI to update and generate optimized queries with reduced resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional query generation methods are used for disparate data sources, then queries can be executed, but computing resource consumption increases excessively
Solution Approach 1:
The system pre-generates multiple possible queries with different attributes and intents before actual data retrieval. By preparing query templates and predicting their resource consumption characteristics in advance, the system avoids executing inefficient queries, thereby reducing computing resource consumption while maintaining productivity.
Solution Approach 2:
The system creates virtual copies of queries with different attributes and evaluates them in a simulated environment using heat maps. This allows the system to identify the most resource-efficient query without executing all possible queries against the actual data sources, reducing computational overhead while preserving query execution effectiveness.
2Adaptability or versatility
If multiple attributes are derived from data lakes, then data searching and reporting capabilities improve, but the complexity of developing and maintaining these attributes increases
Solution Approach 1:
The system creates a universal attribute framework where derived attributes from data lakes can be reused across multiple applications and query types. By establishing a common set of attributes that serve multiple purposes, the system enhances data searching and reporting capabilities while reducing the complexity of developing and maintaining separate attributes for different applications.
Solution Approach 2:
The system implements automated query optimization that self-adjusts attribute selection and query generation based on observed usage patterns and resource consumption metrics. This self-service mechanism reduces manual intervention for attribute maintenance while improving adaptability to different data searching requirements.
3Loss of energy
If queries are optimized based on heat map analysis, then computing resource consumption decreases, but the time required for query optimization increases
Solution Approach 1:
The system applies partial optimization by focusing heat map analysis and query optimization efforts only on the most resource-intensive query patterns and attributes. Rather than optimizing all queries equally, the system identifies and optimizes only those queries that would benefit most, reducing overall computing resource waste while minimizing the time investment required for optimization.
Solution Approach 2:
The system implements continuous feedback loops where query execution results and resource consumption metrics are fed back into the heat map analysis. This feedback mechanism allows the system to progressively refine query optimization over time, reducing computing resource waste while the initial optimization overhead decreases as the system learns from historical data.
Data Source
AI summary
Systems, computer program products, and methods are described herein for generating optimized queries for disparate data sources. The present invention is configured to identify a query; determine, based on a parsing of the query, at least one attribute and at least one intent of the query; generate a map comprising the query, the intent, and the attribute; apply the map to a cluster of maps, wherein the cluster of maps comprises the intent associated with a secondary attribute different from the attribute, and a secondary query; generate, based on the cluster of maps, a heat map of the cluster of maps, wherein the heat map comprises an indication of a computer processing consumption of a query with the intent and the attribute, and the secondary intent with the secondary attribute and the secondary query; and determine, based on the heat map, an optimized query for the intent.