Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to database management systems and particularly to a system and a method for self-optimizing artificial intelligence-based databases.
Description of Related Art
[002] In recent years, modern database management systems (DBMS) have evolved to handle large-scale, complex data workloads across distributed and cloud environments. These systems often incorporate performance tuning mechanisms, such as indexing strategies, caching, and execution plan optimizations. Commercial solutions, including those from major providers like Oracle, Amazon, and IBM, have integrated machine learning models to enhance query planning, detect anomalies, and recommend indexing improvements. However, these solutions typically react to performance degradation after it occurs, rather than proactively preventing it.
[003] Additionally, inefficient SQL queries continue to pose performance bottlenecks, consuming excessive compute resources and increasing latency. Existing systems lack the ability to dynamically rewrite queries, prefetch frequently accessed data, or allocate infrastructure in real time based on predictive analytics.
[004] There is thus a need for an improved and advanced system for self-optimizing artificial intelligence-based databases that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[005] Embodiments in accordance with the present invention provide a system for self-optimizing artificial intelligence-based databases. The system comprises a data capturing unit configured to collect historical Structured Query Language (SQL) query logs from a source, and a processing unit in communication with the data capturing unit. The processing unit is configured to identify recurring query patterns by analyzing the collected historical SQL query logs using a machine learning model. The processing unit further preloads data into a memory based on the identified recurring query patterns, monitors real-time execution of incoming SQL queries, detects inefficient queries from the incoming SQL queries based on execution parameters including latency, Central Processing Unit (CPU) usage, and memory consumption. The processing unit improves execution efficiency by revising the detected inefficient queries in real time using predefined optimization rules and automatically adjusts the allocation of computational resources based on the improved execution efficiency of the revised inefficient queries.
[006] Embodiments in accordance with the present invention further provide a method for self-optimizing performance of a database, comprising collecting historical Structured Query Language (SQL) query logs from a source, identifying recurring query patterns by analyzing the collected historical SQL query logs using a machine learning model, preloading data into a memory based on the identified recurring query patterns, monitoring real-time execution of incoming SQL queries, detecting inefficient queries from the incoming SQL queries based on execution parameters including latency, Central Processing Unit (CPU) usage, and memory consumption, improving execution efficiency by revising the detected inefficient queries in real time using predefined optimization rules, and automatically adjusting the allocation of computational resources based on the improved execution efficiency of the revised inefficient queries.
[007] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for self-optimizing artificial intelligence-based databases that proactively enhance database performance by predicting query patterns and optimizing resources in real time.
[008] Next, embodiments of the present application may provide automatic query refactoring capabilities that revise inefficient queries on the fly, thereby improving execution efficiency without manual intervention.
[009] Next, embodiments of the present application may provide intelligent preloading of frequently accessed data into high-speed memory, reducing query latency and improving response times.
[0010] Next, embodiments of the present application may provide real-time autoscaling of computational resources using artificial intelligence, enabling dynamic adjustment to workload changes and reducing operational costs.
[0011] Next, embodiments of the present application may provide an alerting mechanism to notify administrators when query performance anomalies exceed predefined thresholds, ensuring timely intervention and system reliability.
[0012] These and other advantages will be apparent from the present application of the embodiments described herein.
[0013] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0015] FIG. 1 depicts a block diagram of a system for self-optimizing artificial intelligence-based databases, according to an embodiment of the present invention;
[0016] FIG. 2 illustrates components of a processing unit of the system for self-optimizing artificial intelligence-based databases, according to an embodiment of the present invention; and
[0017] FIG. 3 illustrates a flowchart for a method for self-optimizing artificial intelligence-based databases, according to an embodiment of the present invention.
[0018] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0019] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0020] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0021] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0022] FIG. 1 depicts a block diagram of a system 100 for self-optimizing artificial intelligence-based databases, according to an embodiment of the present invention. The system 100 may be configured to dynamically analyze and optimize Structured Query Language (SQL) workloads across distributed, cloud-based, or hybrid environments. The system 100 may employ one or more machine learning models to learn from historical query execution data and apply this intelligence to future workload management, thereby reducing query latency, improving processing efficiency, and minimizing resource overhead. The system 100 may be configured to detect inefficient queries in real time, and refactor the detected inefficient queries for improved performance. The system 100 may further be configured to proactively adjust computational infrastructure based on observed and predicted load conditions.
[0023] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance processing speed and system efficiency. The system 100 may comprise a data capturing unit 102, a processing unit 104, a memory 106, a communication interface 108, and an alert engine 110.
[0024] The data capturing unit 102 may be configured to continuously collect historical Structured Query Language (SQL) query logs from one or more database sources. The historical SQL query logs may include metadata such as query structure, execution time, CPU and memory utilization, and data access patterns. The collected historical SQL query logs may be transmitted to the processing unit 104 for further analysis. The data capturing unit 102 may be designed to operate in near real-time or batch modes and may support integrations with standard logging frameworks and database monitoring Application Programmable Interfaces (APIs).
[0025] In an embodiment of the present invention, the processing unit 104 may be configured to receive, analyze, and interpret the historical and real-time data collected by the data capturing unit 102. The processing unit 104 may include programming modules (as shown in FIG. 2) for executing instructions related to the output of the system 100, such as detecting query inefficiencies, applying optimization rules, preloading frequently accessed data, and scaling resources dynamically. These modules may also include trained machine learning models that infer usage patterns and predict upcoming query demands. Embodiments of the present invention are intended to include or otherwise cover any suitable output of the processing unit 104, including known, related art, and/or later developed technologies.
[0026] The suitable output may include, but is not limited to the modified SQL queries optimized for improved performance based on historical execution patterns, recommendations or automated decisions for preloading specific datasets into memory 106. The suitable output may further be alerts to database administrators when query anomalies or inefficiencies exceed a predefine threshold, and commands for auto-provisioning or de-provisioning virtual machines, containers, or serverless compute instances as per current or forecasted resource demands.
[0027] The processing unit 104 may be, but is not limited to, a Programmable Logic Controller (PLC), a microprocessor, a development board, and so forth. In some embodiments, the processing unit 104 may also comprise system-on-chip (SoC) architectures, field-programmable gate arrays (FPGAs), single-board computers, embedded controllers, cloud-native orchestration engines, virtualized container hosts, and similar computer hardware. Embodiments of the present invention are intended to include or otherwise cover any type of processing unit 104, including known technologies, related art, and/or later developed technologies. In an embodiment of the present invention, the detailed architecture and function of the processing unit 104 may further be explained in conjunction with FIG. 2.
[0028] The processing unit 104 may employ a machine learning model to analyze the historical Structured Query Language (SQL) query logs collected by the data capturing unit 102. The machine learning model may be trained on performance indicators such as execution latency, central processing unit (CPU) usage, memory consumption, query throughput, and response times. The machine learning model may be implemented using a recurrent neural network that learns sequential dependencies in query behavior, or a transformer-based deep learning architecture that captures complex contextual relationships across query sequences. In some cases, the model may be based on a decision tree algorithm such as a gradient boosting machine or a random forest, trained to classify inefficient query patterns and recommend optimizations. The processing unit 104 may use the output of the machine learning model to detect recurring patterns, predict future query loads, refactor inefficient SQL queries in real time, and initiate preloading of frequently accessed data. The machine learning model may be continuously retrained using live system performance data to enhance its predictive accuracy and adaptability.
[0029] In an embodiment of the present invention, the memory 106 may be any suitable computer-readable medium configured to store the historical SQL logs, intermediate processing data, machine learning model outputs, and frequently accessed data blocks preloaded for performance optimization. The memory 106 may comprise volatile memory such as Random Access Memory (RAM) for high-speed temporary access, non-volatile memory such as flash drives or Solid-State Drives (SSD) for persistent storage, or a combination thereof depending on the performance requirements of the system. In cloud-based or distributed deployments, the memory 106 may also represent remote object storage systems, distributed cache layers, or persistent volumes accessible via networked interfaces. The memory 106 may be optimized to support high input/output (I/O) operations required by predictive data access and query execution acceleration. Embodiments of the present invention are intended to include or otherwise cover any type of memory architecture, including known, related art, and/or later developed technologies suited for dynamic and intelligent data handling.
[0030] In an embodiment of the present invention, the communication interface 108 may be configured to enable data exchange between the system 100 and other external entities such as cloud platforms, database engines, orchestration services, and administrative dashboards. The communication interface 108 may include, but is not limited to, Ethernet ports for physical network access, wireless communication modules for flexible connectivity, Representational State Transfer (RESTful) Application Programming Interfaces (APIs) for system integration, message queues for asynchronous data flow, and cloud-native connectors for infrastructure-level interaction. The communication interface 108 may further be configured to support encryption protocols such as Transport Layer Security (TLS) or Secure Sockets Layer (SSL) to ensure secure transmission of sensitive query logs and optimization feedback. Additionally, the communication interface 108 may support bidirectional data flow to facilitate real-time alerts, trigger auto-scaling actions, receive updated optimization rules, and report anomalies to system administrators or monitoring services.
[0031] The alert engine 110 may be configured to continuously monitor the performance metrics and optimization results generated by the processing unit 104. In an embodiment of the present invention, the alert engine 110 may be designed to detect anomalies or deviations in query performance that exceed the predefined threshold. Upon detecting such anomalies, the alert engine 110 may generate real-time notifications or alerts to database administrators and/or system operators through various communication channels including email, SMS, dashboards, or system logs. The alert engine 110 may also be configurable to categorize alerts based on severity and recommend corrective actions or trigger automated remediation workflows.
[0032] FIG. 2 illustrates components of the processing unit 104 for the system 100, according to an embodiment of the present invention. In an embodiment of the present invention, the processing unit 104 may comprise computer-executable instructions in the form of programming modules including a data acquisition module 200, a data preprocessing module 202, a query pattern recognition module 204, a data preloading module 206, a query optimization module 208, and a resource scaling module 210.
[0033] In an embodiment of the present invention, the data acquisition module 200 may be configured to receive historical Structured Query Language (SQL) query logs and real-time query execution data from the data capturing unit 102. The data acquisition module 200 may be further configured to extract raw query data along with associated execution metadata. Upon successful acquisition, the data acquisition module 200 may be configured to generate a data processing signal and may transmit the data processing signal to the data preprocessing module 202.
[0034] In an embodiment of the present invention, the data preprocessing module 202 may be configured to be activated upon receiving the data processing signal from the data acquisition module 200.
[0035] In an embodiment of the present invention, the data preprocessing module 202 may be configured to normalize, clean, and format the received query data and execution parameters for further analysis. The data preprocessing module 202 may be further configured to extract performance indicators such as average query latency, Central Processing Unit (CPU) usage, memory consumption, and query execution times. Upon extracting the performance parameters, the data preprocessing module 202 may be configured to generate a pattern recognition signal and may transmit the generated pattern recognition signal to the query pattern recognition module 204.
[0036] In an embodiment of the present invention, the query pattern recognition module 204 may be configured to be activated upon receiving the pattern recognition signal from the data preprocessing module 202. In an embodiment of the present invention, the query pattern recognition module 204 may be configured to analyze the normalized historical query data using a machine learning model to identify frequently recurring query structures, parameters, or table joins. The machine learning model may include, but is not limited to, the recurrent neural networks (RNNs), the decision tree-based classifiers, or the transformer-based architectures trained on prior query performance datasets.
[0037] Upon detecting recurring query patterns, the query pattern recognition module 204 may transmit the detected patterns to the data preloading module 206 for proactive memory allocation. The system 100 may continue to perform real-time monitoring and feedback loops for iterative learning and optimization.
[0038] In an embodiment of the present invention, the data preloading module 206 may be configured to be activated upon receiving the recurring query patterns from the query pattern recognition module 204. The data preloading module 206 may be configured to preload data into the memory 106 based on the detected frequently accessed data blocks or query components. This preloading may include caching the most frequently accessed tables, indices, or data partitions to reduce latency for incoming queries. The data preloading module 206 may generate a preload completion signal upon successful data loading and transmit it to the query optimization module 208.
[0039] In an embodiment of the present invention, the query optimization module 208 may be configured to monitor the real-time execution of incoming SQL queries by receiving live query execution parameters including latency, Central Processing Unit (CPU) usage, memory consumption, and other performance indicators.
[0040] Upon detecting queries exhibiting inefficient execution based changes in historical execution performance from baseline resource usage and/or baseline metrics, the query optimization module 208 may apply predefined or dynamically updated optimization rules to revise the inefficient queries in real time. Such revisions may include modifying SQL clauses such as JOIN order, subquery restructuring, or applying index hints, with the objective of improving execution efficiency. In an embodiment of the present invention, the inefficient queries may be dynamically updated based on a continuous feedback from the execution parameters.
[0041] The query optimization module 208 may further generate an optimization feedback signal indicating the improvement status of revised queries and transmit the optimization feedback signal to the resource scaling module 210.
[0042] In an embodiment of the present invention, the resource scaling module 210 may be configured to automatically adjust the allocation of computational resources based on the improved execution efficiency data received from the query optimization module 208. The resource scaling module 210 may dynamically provision or deprovision resources such as virtual machines, containers, or serverless compute instances, in accordance with the projected workload and efficiency metrics. The resource scaling module 210 may further monitor resource usage trends to optimize operational cost and system performance, and may trigger alerts to database administrators via the alert engine 110 when anomalies persist beyond the predefined threshold despite optimization efforts. The predefined threshold may be set by the system administrators based on historical performance data, service level agreements (SLAs), dynamic baseline metrics calculated by the system 100, and so forth. The predefined threshold may be, but is not limited to, maximum allowable query latency, CPU utilization percentage, memory consumption limits, or a composite score derived from multiple performance indicators.
[0043] In an embodiment of the present invention, the processing unit 104 may continuously perform iterative learning and optimization through feedback loops between the query pattern recognition module 204, query optimization module 208, and resource scaling module 210, thereby enabling self-optimization of database performance in hybrid cloud or multi-tenant environments.
[0044] In an exemplary embodiment of the present invention, a cloud-hosted e-commerce database may be handling high volumes of customer search queries during a seasonal sale event. The system 100 may collect historical Structured Query Language (SQL) logs that reveal recurring search queries involving complex JOIN operations across inventory, pricing, and product description tables. During the event, the data capturing unit 102 may record a sudden surge in query execution time, CPU usage, and memory consumption. The processing unit 104 may analyze the real-time data using the machine learning model and identify the frequent query patterns. The frequently accessed product and pricing data may be preloaded into the high-speed memory 106 to improve response times. The system 100 may detect inefficient queries involving nested subqueries and suboptimal JOIN orders, and the processing unit 104 may revise the queries by reordering clauses and applying relevant index hints based on the predefined optimization rules. The resource scaling module 210 may provision additional virtual machines or containers to handle the increased workload. As a result, the system 100 may achieve optimized query performance, reduced latency, and improved resource utilization.
[0045] FIG. 3 illustrates a flowchart for a method 300 for adaptive database indexing, according to an embodiment of the present invention. The method 300 may be implemented by the system 100 for dynamically and automatically optimizing the query performance and the resource allocation in a dynamic and automated manner.
[0046] At step 302, the system 100 may collect the historical Structured Query Language (SQL) query logs and real-time query execution data from the connected data sources via the data capturing unit 102.
[0047] At step 304, the system 100 may preprocess the collected data to normalize, clean, and extract the relevant performance parameters such as query latency, CPU usage, and memory consumption.
[0048] At step 306, the system 100 may analyze the preprocessed data using the machine learning model to identify the recurring query patterns and predict the frequently accessed data segments.
[0049] At step 308, the system 100 may preload the identified frequently accessed data into the high-speed memory 106 to reduce query latency and improve response time.
[0050] At step 310, the system 100 may monitor the execution of the incoming SQL queries in real time to detect the inefficient queries based on the predefined execution parameters and thresholds.
[0051] At step 312, the system 100 may automatically revise the detected inefficient queries by applying the predefined optimization rules and dynamically adjust the computational resource allocation to optimize system performance and reduce operational costs.
[0052] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0053] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A system for self-optimizing artificial intelligence-based databases, the system (100) comprising:
a data capturing unit (102) configured to collect historical Structured Query Language (SQL) query logs from a source; and
a processing unit (104) in communication with the data capturing unit (102), characterized in that the processing unit (104) configured to:
identify recurring query patterns by analyzing the collected historical SQL query logs using a machine learning model;
preload data into a memory (106) based on the identified recurring query patterns;
monitor real-time execution of incoming SQL queries;
detect inefficient queries from the incoming SQL queries based on execution parameters; wherein the execution parameters are selected from a latency, a Central Processing Unit (CPU) usage, a memory consumption, and so forth;
improve an execution efficiency by revising the detected inefficient queries in real time using predefined optimization rules; and
automatically adjust allocation of computational resources based on the improved execution efficiency of the revised inefficient queries.
2. The system as claimed in claim 1, wherein the machine learning model is selected from a recurrent neural network (RNN), a transformer, a decision tree-based algorithm trained on historical query performance data, or a combination thereof.
3. The system as claimed in claim 1, wherein preloading of the data includes storing most frequently accessed data blocks in a high-speed memory data structure.
4. The system as claimed in claim 1, wherein the inefficient queries are detected based on changes in historical execution performance from baseline resource usage.
5. The system as claimed in claim 1, wherein the inefficient queries are revised by modifying SQL clauses such as JOIN order, subquery structure, or index hints based on optimization rules or model output.
6. The system as claimed in claim 1, wherein automatically adjusting the computational resources includes provisioning virtual machines, containers, serverless compute instances based on projected workload needs, or a combination thereof.
7. The system as claimed in claim 1, comprising an alert engine (110) to notify database administrators when query anomalies exceed a predefined threshold even after optimization.
8. The system as claimed in claim 1, wherein the optimization rules for revising inefficient queries are dynamically updated based on continuous feedback from the execution parameters.
9. The system as claimed in claim 1, wherein the processing unit (104) is configured to be operable in hybrid cloud environments and support multi-tenant architectures.
10. A method for self-optimizing performance of a database, comprising steps of:
collecting historical Structured Query Language (SQL) query logs from a source;
identifying recurring query patterns by analyzing the collected historical SQL query logs using a machine learning model;
preloading data into a memory (106) based on the identified recurring query patterns;
monitoring real-time execution of incoming SQL queries;
detecting inefficient queries from the incoming SQL queries based on execution parameters; wherein the execution parameters are selected from including a latency, a Central Processing Unit (CPU) usage, a memory consumption, and so forth;
improving an execution efficiency by revising the detected inefficient queries in real time using predefined optimization rules; and
automatically adjusting allocation of computational resources based on the improved execution efficiency of the revised inefficient queries.

Date: May 19, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant