AutoCE: An Intelligent Model Advisor Revolutionizing Cardinality Estimation for Databases through Advanced Deep Metric Learning and Incremental Learning Techniques

Practical Solutions and Value of Cardinality Estimation in Databases Cardinality Estimation (CE) is essential for tasks like query planning, cost estimation, and optimization in databases. Accurate CE ensures efficient query execution. Benefits of Machine Learning in CE Machine Learning improves CE accuracy and reduces processing time, enhancing the performance of database management systems. Challenges in CE and Existing Methods Diverse datasets present challenges in CE. Existing methods struggle to generalize performance effectively. Introducing AutoCE for Intelligent Model Selection AutoCE automatically selects the best CE model based on dataset features, significantly improving performance without exhaustive training. AutoCE’s Core Technology and Performance AutoCE extracts dataset features, trains a graph encoder, and uses incremental learning for better predictions. It outperforms traditional models in accuracy and efficiency. Key Takeaways from AutoCE Research AutoCE boosts efficiency, accuracy, and reduces latency in database systems. It adapts to different dataset characteristics and integrates well with PostgreSQL v13.1. Conclusion AutoCE uses advanced deep-learning techniques to enhance CE model selection, transforming database query optimization and improving accuracy and efficiency in data-intensive applications. AI Solutions for Your Company Utilize AutoCE to stay competitive and improve work processes with AI. Identify automation opportunities, define KPIs, select suitable AI tools, and implement gradually for successful integration. Contact us for AI KPI management advice and insights on leveraging AI for sales processes and customer engagement.