MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202611108925 A) filed by Sunil Karmakar; Ankit Srivastava; Raviteja Patnam; and Ankur Singh on September 10, 2026, for Intelligent Cloud Data Warehouses: Auto-Scaling And Dynamic Query Optimization Using Ai..
Inventors include Sunil Karmakar; Ankit Srivastava; Raviteja Patnam; and Ankur Singh.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: [505] The present invention introduces an advanced intelligent cloud data warehouse framework that integrates AI-based workload analysis with auto-scaling mechanisms and dynamic query optimization protocols, facilitating real-time resource provisioning, dynamic execution-plan optimization, and robust performance confirmation while maintaining seamless cloud integration and operational accuracy for consistent data-warehousing applications. [510] The comprehensive warehousing framework employs adaptive machine-learning algorithms and workload-forecasting protocols, utilising embedded computational processing arrays and cost-efficient resource-allocation systems to ensure timely capacity identification, enhanced query understanding, and optimal execution reliability while maintaining continuous workload monitoring capabilities across distributed compute clusters. [515] The integrated methodology combines multi-source telemetry ingestion techniques with artificial-intelligence-driven pattern recognition systems, leveraging variable-confidence demand signals and multi-factor performance indicators to optimise scaling procedures and query workflows for maximum execution accuracy and minimal resource uncertainty during critical analytical operations. [520] The novel responsive warehousing architecture features engineered high-throughput query-processing components with specialised workload-fingerprinting protocols, enabling complex multi-stage optimization verification while ensuring performance consistency and cost optimisation across various cloud applications without compromising system reliability. [525] The innovative design incorporates strategic validation mechanisms for enhanced workload identification and operational security, utilising optimised multi-model systems and adaptive resource technology to ensure legitimate capacity assignment while maintaining functionality across diverse cloud environments and query scenarios. [530] Implementation methodology emphasises scalable resource integration and efficient optimization sequences, implementing interactive monitoring measures and predictive modelling algorithms to achieve superior capacity determination, enhanced query identification, and resource-waste prevention while ensuring technological simplicity during warehouse operation. [535] The system demonstrates exceptional adaptability through comprehensive integration of workload identification protocols and intelligent optimization technologies, validating its effectiveness across various cloud data warehouse configurations and analytical scenarios while maintaining consistent performance and operational efficiency under diverse conditions. [540] The developed framework enables sustainable and reliable operation of cloud data warehouses through streamlined, AI-powered scaling and optimization systems, providing significant advantages over traditional static-provisioning approaches through adaptive validation mechanisms, predictive identification protocols, and improved capacity assignment while maintaining superior query performance during critical analytical execution.
Disclaimer: Curated by HT Syndication.
