A system for AI-driven optimization of data models for efficient query processing.

Abstract

A system for AI-driven optimization of data models for efficient query processing, including: a query analysis module configured to dynamically analyze real-time query loads and identify patterns within the database; an AI optimization engine integrated with machine learning techniques, configured to generate optimized query execution plans based on the identified query patterns and real-time workload analysis; a schema adaptation module that adapts database schema structures through a balance between normalization and denormalization to improve query performance without manual intervention; an indexing and partitioning module that automatically selects and manages indexing strategies and partitions data to minimize data skew and ensure optimal workload distribution; a resource management module that dynamically allocates CPU, memory, and disk I / O resources to enable efficient query execution and load balancing in the system.

Description

[0001] The present invention relates to database management and query processing. More specifically, it is a system that uses artificial intelligence (AI) to dynamically optimize data models, improve query performance, reduce execution time, and improve resource efficiency.

[0002] Efficient query processing is fundamental to modern database management systems (DBMS), especially for large-scale applications such as cloud computing, business intelligence, real-time analytics, and big data processing. As databases grow larger and more complex, ensuring optimal query performance becomes increasingly difficult. Traditional database optimization techniques rely on static schema designs, indexing strategies, materialized views, query caching, and partitioning methods to improve query execution. However, these approaches often require significant manual intervention and cannot be dynamically adapted to changing data patterns, query loads, and workload fluctuations, resulting in inefficient database performance.

[0003] Traditional database systems rely on predefined rules and cost-based query optimizers to improve execution plans. However, these optimizers have limited ability to effectively handle dynamic queries. While schema normalization ensures data integrity and minimizes redundancy, it often results in complex joins that increase query execution time. On the other hand, denormalization can improve performance for certain queries, but at the cost of increased memory requirements, redundant data, and higher maintenance overhead. Similarly, indexing techniques such as B-trees, hash indexes, and bitmap indexes improve data retrieval speed but impose tradeoffs in memory consumption and index maintenance overhead, particularly during data updates and inserts.

[0004] Partitioning techniques are often used to distribute large data sets across multiple storage locations to improve query response times. However, improper partitioning can lead to data skew, resulting in uneven load distribution and reduced system efficiency. Furthermore, materialized views and query caching reduce query execution time by storing pre-computed results, but they require frequent updates and maintenance, increasing computational overhead and memory requirements. While these conventional optimization techniques are effective in static environments, they struggle to handle dynamic workloads and evolving data structures.

[0005] One of the major drawbacks of traditional query optimization methods is their inability to adapt to real-time workload changes. Since most database optimizations are manually configured, they require constant monitoring and tuning by database administrators, resulting in high operational costs. Furthermore, traditional optimization approaches cannot effectively handle complex queries that involve multiple joins, aggregations, and nested subqueries, often resulting in suboptimal execution plans and increased query latency. Another major challenge is the inefficient use of compute resources, as poorly optimized queries result in higher CPU, memory, and disk I / O consumption, negatively impacting overall system performance.

[0006] Furthermore, traditional database management systems do not incorporate artificial intelligence (AI) or machine learning (ML) techniques for self-learning and continuous improvement. Traditional query optimizers rely on historical execution plans and static heuristics, limiting their ability to predict future performance bottlenecks and proactively adapt data models. As a result, these systems are unable to dynamically optimize indexing, schema structures, and query execution strategies based on evolving query patterns and data workloads.

[0007] To address these challenges, an AI-driven optimization system is needed that continuously analyzes query execution patterns, dynamically adapts database schema structures, and leverages machine learning algorithms to improve query performance. Such a system would eliminate manual tuning by autonomously identifying inefficiencies, recommending indexing and partitioning strategies, and refining query execution plans based on real-time performance data. By integrating AI-driven insights, the system can proactively optimize data models, reduce query execution time, minimize computational overhead, and improve overall resource efficiency.The proposed invention introduces a novel AI-based approach to database optimization and provides a self-learning, adaptive system that ensures efficient query processing in modern database environments.

[0008] To solve this problem, the present invention provides a system for AI-driven optimization of data models for efficient query processing.

[0009] The AI-driven data model optimization system for efficient query processing can dynamically optimize query execution plans based on real-time workload analysis, select the most efficient execution path, and minimize query execution time without manual intervention.

[0010] The AI-driven data model optimization system for efficient query processing can analyze query patterns and automatically restructure database schemas to improve performance, dynamically balancing normalization and denormalization to ensure both data integrity and query efficiency.

[0011] The AI-driven data model optimization system for efficient query processing can automate index selection and management by predicting the most effective indexing strategy based on query trends and implementing AI-driven data partitioning techniques that minimize data skew and ensure balanced workload distribution.

[0012] The AI-driven data model optimization system for efficient query processing can reduce computational overhead by efficiently utilizing CPU, memory, and disk I / O resources during query execution and ensuring optimal resource utilization through dynamic load balancing across the system.

[0013] The system for AI-driven optimization of data models for efficient query processing can integrate continuous performance monitoring, enabling the system to predict potential bottlenecks and proactively adjust data models, indexing strategies, and query execution plans based on evolving query patterns and system performance data.

[0014] The system for AI-driven optimization of data models for efficient query processing is capable of handling diverse workloads and adapting to large, complex databases to ensure long-term effectiveness and optimization as data volumes grow.

[0015] In one embodiment, a system for AI-driven data model optimization for efficient query processing is provided. The system leverages artificial intelligence (AI) and machine learning (ML) techniques to dynamically analyze query workloads and optimize data models, indexing strategies, and query execution plans in real time. The system eliminates the need for manual intervention, thereby improving database performance, reducing query execution time, and improving overall resource utilization. The system continuously monitors query patterns, adjusts database schemas, and optimizes indexing, partitioning, and resource allocation based on evolving data and query loads.By leveraging machine learning models, the system predicts the most efficient execution paths and dynamically adjusts database structures to ensure balanced resource utilization and minimize data skew. The system includes automatic query optimization, dynamic schema adaptation, intelligent indexing and partitioning, resource optimization, and proactive performance monitoring. Scalable and adaptable, the system is capable of handling large databases and complex query patterns. Its AI-driven approach ensures continuous self-improvement, allowing the system to predict and proactively address performance bottlenecks, resulting in sustained high efficiency and lower operating costs.

[0016] The invention is explained again below with reference to the figure. It shows: Fig. : a system for AI-driven optimization of data models for efficient query processing

[0017] Fig.shows a system for AI-driven data model optimization for efficient query processing. The system for AI-driven data model optimization for efficient query processing includes a query analysis module, an AI optimization engine, a schema adaptation module, an indexing and partitioning module, and a resource management module. The query analysis module is configured to dynamically analyze real-time query workloads and identify patterns within the database. The AI ​​optimization module is integrated with machine learning techniques. The AI ​​optimization engine is configured to create optimized query execution plans based on the identified query patterns and real-time workload analysis.The schema adaptation module is configured to adjust database schema structures by balancing normalization and denormalization to improve query performance without manual intervention. The indexing and partitioning module is configured to automatically select and manage indexing strategies and partition data to minimize data skew and ensure optimal workload distribution. The resource management module is configured to dynamically allocate CPU, memory, and disk I / O resources for efficient query execution and load balancing across the system. The AI ​​optimization engine is also configured to continuously improve query execution by learning from historical query performance data and adjusting optimization strategies in real time.The schema adaptation module uses machine learning algorithms to predict the optimal schema structure based on query complexity, frequency, and data usage patterns. The indexing and partitioning module leverages AI-driven predictive models to select the most effective indexing strategy and partitioning scheme based on query load, ensuring balanced data distribution and query efficiency. The resource management module monitors the unit's resource usage and dynamically reallocates resources to optimize performance, reduce computational overhead, and ensure a balanced system load during query execution. The system incorporates a self-improvement and learning module, which enables it to continuously improve its optimization techniques based on feedback from previous query executions.The system features a proactive performance monitoring module that predicts and resolves potential performance bottlenecks before they occur, ensuring efficient query processing under high-load conditions. The system includes a scalability and adaptability module that ensures efficient query processing in distributed databases and cloud environments.

[0018] Optimizing query processing in a database management system using artificial intelligence includes dynamically analyzing query workloads in real time to identify query patterns; using machine learning models to generate optimized query execution plans based on real-time query analysis; automatically adapting database schema structures by balancing normalization and denormalization for improved performance; predicting and implementing indexing and partitioning strategies to ensure efficient data access and distribution; and allocating and managing system resources to optimize query execution performance and system load balancing.The optimization process is continuously improved based on feedback from historical query performance, resulting in evolving optimization strategies. List of reference symbols 100 systems

Claims

[1] A system for AI-driven optimization of data models for efficient query processing, comprising: a query analysis module configured to dynamically analyze real-time query loads and identify patterns within the database; an AI optimization engine integrated with machine learning techniques, configured to generate optimized query execution plans based on the identified query patterns and real-time workload analysis; a schema adaptation module that adapts database schema structures through a balance between normalization and denormalization to improve query performance without manual intervention; an indexing and partitioning module that automatically selects and manages indexing strategies and partitions data to minimize data skew and ensure optimal workload distribution; a resource management module that dynamically allocates CPU, memory, and disk I / O resources to enable efficient query execution and load balancing in the system. [2] The system of claim 1, wherein the AI ​​optimization engine is further configured to continuously improve query execution by learning from historical query performance data and adapting optimization strategies in real time. [3] The system of claim 1, wherein the schema adaptation module uses machine learning algorithms to predict the optimal schema structure based on query complexity, frequency, and data usage patterns. [4] The system of claim 1, wherein the indexing and partitioning module uses AI-driven predictive models to select the most effective indexing strategy and partitioning scheme based on query volume, thereby ensuring balanced data distribution and query efficiency. [5] The system of claim 1, wherein the resource management module monitors the usage of the system resources and dynamically reallocates the resources to optimize performance, reduce computational overhead, and ensure a balanced system load during query execution. [6] The system of claim 1, wherein the system integrates the self-improvement and learning module that enables it to continuously improve its optimization techniques based on feedback from previous query executions. [7] The system of claim 1, wherein the system includes a proactive performance monitoring module that predicts and resolves potential performance bottlenecks before they occur, thereby ensuring efficient query processing under high-load conditions. [8] The system of claim 1, wherein the system includes a scalability and adaptation module that ensures efficient query processing in distributed databases and cloud environments.

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