A system of AI-powered query optimization and self-optimizing data pipelines

Abstract

A system (100) for AI-assisted query optimization and self-optimizing data pipelines, comprising: a query analysis and classification module configured to monitor, log, and categorize incoming queries based on execution complexity, type, and frequency; an AI-based query execution optimizer that uses machine learning models to analyze historical query performance and recommend dynamically optimized execution plans; a self-optimizing data pipeline module configured to monitor and adapt ETL workflows by modifying transformation logic, workload balancing, and execution sequences; an adaptive indexing and storage management module configured to dynamically restructure indexing strategies, including predictive indexing and partitioning, to improve query performance; an intelligent caching and pre-fetching module configured to anticipate frequently used queries and pre-load relevant data to reduce redundant computations; a workload balancing and resource optimization module configured to dynamically distribute query loads among computing resources based on the constraints of the real-time system (100); an anomaly detection and performance monitoring module configured to continuously track query execution metrics, identify inefficiencies, and mitigate performance bottlenecks using AI-based predictive analytics; a reinforcement learning-based query optimization module configured to iteratively refine query execution strategies based on real-time feedback and evolving data properties; a multi-cloud and cross-platform optimization engine configured to ensure seamless operation across heterogeneous database management systems and cloud platforms.

Description

[0001] The present invention relates to data management, and more particularly to a system and method that uses artificial intelligence (AI) to optimize database queries and dynamically improve the performance of data pipelines in real time.

[0002] The increasing reliance on data-intensive applications in various industries such as finance, healthcare, e-commerce, and telecommunications has led to an exponential increase in data generation and processing. Organizations today deploy sophisticated database management systems (DBMS) and data pipelines to efficiently capture, transform, and analyze structured and unstructured data. The ability to optimally execute queries and process data in real time is critical for organizations seeking to gain actionable insights, improve operational efficiency, and make data-driven decisions. However, traditional query optimization techniques and data pipeline management methods have significant limitations that limit their ability to adapt to the dynamic nature of modern data environments.

[0003] Traditional query optimization relies on static, rule-based approaches, including cost-based query planners, heuristic-driven optimizers, and predefined execution plans. These techniques determine query execution strategies based on a set of preconfigured rules and cost estimates, without dynamically adapting to changes in workload, evolving data distributions, or fluctuating system constraints. Such rigidity often leads to suboptimal performance, as queries that are efficient under one set of conditions can become inefficient as data volume and structure evolve. Furthermore, indexing strategies in traditional systems are typically predefined and require manual intervention for tuning, updating, and optimization based on workload patterns.Failure to adjust indexing strategies in real time often results in inefficient data retrieval, increased disk I / O, and higher query execution latency.

[0004] Furthermore, existing data pipelines follow predefined transformation logic and static scheduling mechanisms that are unable to dynamically adapt to varying data input rates, processing loads, and system resources. Traditional ETL (extract, transform, and load) processes execute data transformations based on fixed configurations, leading to inefficiencies when processing real-time data streams, large data sets, or workload peaks. This lack of adaptability often leads to bottlenecks, redundant computations, and excessive resource consumption, ultimately impacting system performance and scalability.

[0005] Another major disadvantage of traditional systems is the significant manual effort required for performance tuning and optimization. Database administrators and data engineers must continuously monitor query execution plans, manually adjust configurations, optimize indexing, and fine-tune execution parameters to maintain performance. This process is not only labor-intensive but also prone to human error, resulting in inconsistent performance improvements and increased operating costs. Furthermore, existing workload management systems lack intelligent mechanisms for efficiently distributing query loads across compute resources, leading to unbalanced resource utilization and higher processing costs.

[0006] Furthermore, traditional query optimization methods fail to integrate real-time learning and adaptive intelligence. They don't leverage historical query execution patterns, predictive analytics, or machine learning techniques to identify performance bottlenecks and proactively adjust optimization strategies. This results in reactive performance optimization, where inefficiencies are only identified and addressed when they cause significant delays or system downtime.

[0007] To solve the problem, the present invention provides a system of AI-assisted query optimization and self-optimizing data pipelines.

[0008] The system's AI-powered query optimization and self-optimizing data pipelines can continuously analyze query execution patterns and dynamically adjust execution plans, indexing strategies, and caching mechanisms to improve performance.

[0009] The system's AI-powered query optimization and self-optimizing data pipelines enable real-time adaptive data pipeline management that automatically adjusts transformation logic, processing order, and resource allocation based on workload fluctuations and system constraints.

[0010] The system's AI-powered query optimization and self-optimizing data pipelines can minimize query processing latency by leveraging machine learning-based cost estimation, predictive indexing, and adaptive workload balancing.

[0011] The system, with AI-powered query optimization and self-optimizing data pipelines, learns from historical queries and dynamically restructures database indexes to optimize data retrieval and minimize storage overhead.

[0012] The system's AI-powered query optimization and self-optimizing data pipelines can anticipate frequently used queries and preload relevant data, reducing redundant computations and disk I / O.

[0013] AI-powered query optimization and self-optimizing data pipelines can reduce the dependence on database administrators and data engineers for performance tuning and enable fully automated, self-optimizing query execution and pipeline management.

[0014] The system's AI-powered query optimization and self-optimizing data pipelines ensure seamless scaling of data processing capabilities for growing data sets and dynamic workloads without compromising efficiency.

[0015] The system's AI-powered query optimization and self-optimizing data pipelines can identify and mitigate performance bottlenecks, deadlocks, and inefficient query execution in real time using AI-powered anomaly detection techniques.

[0016] In one embodiment, a system for AI-powered query optimization and self-optimizing data pipelines is provided. An AI-powered query optimization and self-optimizing data pipeline system that dynamically improves the efficiency of query execution, memory management, and data processing operations. The system leverages machine learning (ML), predictive analytics, and reinforcement learning to optimize query performance and data pipeline operations in real time. The system includes a query analysis and classification module that monitors and categorizes queries based on complexity, execution time, and frequency. The AI-powered query execution optimizer uses ML models to recommend the most efficient execution plans by dynamically adjusting indexing, partitioning, and query structures.The Self-Optimizing Data Pipeline Module enables adaptive ETL workflows by automatically adjusting data transformation logic, workload balancing, and execution order. To increase retrieval speed, the intelligent caching and pre-fetching module predicts frequently used queries and pre-fetches relevant data, minimizing redundant computations. The Load Balancing and Resource Optimization module dynamically distributes compute resources across cloud and on-premises environments to avoid bottlenecks. Furthermore, the Anomaly Detection and Performance Monitoring module continuously tracks execution metrics and detects inefficiencies, deadlocks, and security threats in real time.The system includes a reinforcement learning-based query optimization module that iteratively refines execution strategies based on feedback loops, ensuring long-term performance improvements. The multi-cloud optimization module ensures cross-platform compatibility and optimizes workloads across different DBMSs and distributed environments.

[0017] The invention is explained again below with reference to the figure. It shows: Fig. : a system of AI-powered query optimization and self-optimizing data pipelines.

[0018] Fig.shows a system for AI-assisted query optimization and self-optimizing data pipelines. The system (100) includes a query analysis and classification module, an AI-based query execution optimizer, a self-optimizing data pipeline module, an adaptive indexing and storage management module, an intelligent caching and pre-fetching module, a workload balancing and resource optimization module, an anomaly detection and performance monitoring module, a reinforcement learning-based query optimization module, and a multi-cloud and cross-platform optimization module.The query analysis and classification module, the AI-based query execution optimizer, the self-tuning data pipeline module, an adaptive indexing and memory management module, the intelligent caching and pre-fetching module, the workload balancing and resource optimization module, the anomaly detection and performance monitoring module, the reinforcement learning-based query optimization module, and the multi-cloud and cross-platform optimization module collectively improve query execution efficiency, workload distribution, and adaptive data pipeline management. The query analysis and classification module monitors, logs, and categorizes incoming queries based on execution complexity, type, and frequency. This enables the system to predict workload trends and optimize database operations accordingly.The AI-based Query Execution Optimizer leverages machine learning techniques to analyze historical query performance and determine optimal execution plans by adjusting indexing strategies, partitioning, and caching mechanisms. The self-optimizing data pipeline module dynamically modifies ETL (extract, transform, and load) workflows to balance workloads, improve transformation logic, and adjust execution sequences based on real-time performance metrics. The self-optimizing data pipeline module is integrated with the adaptive indexing and memory management module, which dynamically restructures indexing using predictive modeling to improve query efficiency.The intelligent caching and pre-fetching module uses reinforcement learning models to anticipate frequently used queries and pre-fetch relevant data to minimize redundant computations. Meanwhile, the workload balancing and resource optimization module dynamically distributes compute resources across cloud and on-premises environments, ensuring optimized query execution without overloading specific database nodes. To maintain system efficiency (100), the anomaly detection and performance monitoring module tracks execution metrics and identifies inefficiencies, deadlocks, or potential security threats. The anomaly detection and performance monitoring module works in conjunction with the reinforcement learning-based query optimization module, which iteratively refines query execution strategies using continuous feedback loops.The reinforcement learning-based query optimization module ensures that optimization techniques evolve over time, improving query response times and reducing computational overhead. Finally, the multi-cloud and cross-platform optimization module ensures seamless integration and operation across various database management systems (100), including AWS, Azure, Google Cloud, and hybrid environments. This module adapts query execution strategies to different infrastructure configurations, improving overall performance and scalability. List of reference symbols 100 systems

Claims

[1] A system (100) for AI-assisted query optimization and self-optimizing data pipelines, comprising: a query analysis and classification module configured to monitor, log, and categorize incoming queries based on execution complexity, type, and frequency; an AI-based query execution optimizer that uses machine learning models to analyze historical query performance and recommend dynamically optimized execution plans; a self-optimizing data pipeline module configured to monitor and adapt ETL workflows by modifying transformation logic, workload balancing, and execution sequences; an adaptive indexing and storage management module configured to dynamically restructure indexing strategies, including predictive indexing and partitioning, to improve query performance; an intelligent caching and pre-fetching module configured to anticipate frequently used queries and pre-load relevant data to reduce redundant computations; a workload balancing and resource optimization module configured to dynamically distribute query loads among computing resources based on the constraints of the real-time system (100); an anomaly detection and performance monitoring module configured to continuously track query execution metrics, identify inefficiencies, and mitigate performance bottlenecks using AI-based predictive analytics; a reinforcement learning-based query optimization module configured to iteratively refine query execution strategies based on real-time feedback and evolving data properties; a multi-cloud and cross-platform optimization engine configured to ensure seamless operation across heterogeneous database management systems and cloud platforms. [2] The system (100) of claim 1, wherein the query analysis and classification module further uses natural language processing (NLP) techniques to analyze the query intent. [3] The system (100) of claim 1, wherein the AI-based query execution optimizer incorporates heuristic-based optimization techniques to refine execution plans. [4] The system (100) of claim 1, wherein the self-optimizing data pipeline module optimizes batch and real-time processing using adaptive scheduling techniques. [5] The system (100) of claim 1, wherein the adaptive indexing and storage management module uses predictive modeling to dynamically create and delete indexes. [6] The system (100) of claim 1, wherein the intelligent caching and prefetching module uses reinforcement learning models to anticipate and prioritize prefetching of data. [7] The system (100) of claim 1, wherein the workload balancing and resource optimization module reallocates computing resources in response to real-time performance metrics. [8] The system (100) of claim 1, wherein the anomaly detection and performance monitoring module uses deep learning-based anomaly detection models to flag inefficient query execution. [9] The system (100) of claim 1, wherein the reinforcement learning-based query optimization module applies reward-based learning techniques to optimize execution plans.

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