Adaptive Database Quantitative Data Update via ML Urgency Index
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing database management systems face performance degradation due to outdated quantitative data, which is not adequately addressed by current automatic data collection methods that fail to consider real-time workload and resource contention, leading to inefficient resource usage and delayed performance improvements.
Innovation Solution
A computer program product that trains machine learning models to adaptively update quantitative data in a database system by determining an update urgency index based on real-time query metrics, ensuring updates are performed when necessary and minimizing resource contention, thereby optimizing database query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantitative data is updated frequently to maintain accuracy, then query optimization improves, but resource contention and system overhead increase
Solution Approach 1:
The patent implements dynamic update scheduling where the frequency and timing of quantitative data updates are adjusted based on real-time database workload conditions. The system monitors query patterns, data change rates, and system performance metrics to adaptively determine when updates are necessary, transitioning from static periodic updates to dynamic demand-driven updates that balance accuracy with resource consumption
Solution Approach 2:
The system changes key parameters such as update thresholds, time intervals, and triggering conditions based on observed database behavior. By adjusting these parameters dynamically, the system optimizes the balance between maintaining accurate quantitative data and minimizing the overhead of update operations, allowing the update strategy to evolve with changing workload patterns
2Productivity
If quantitative data is updated in real-time to reflect current workload, then query processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where query performance metrics, data change rates, and workload patterns are continuously monitored and fed back to the update scheduling system. This feedback loop enables the system to automatically adjust update timing and frequency based on actual performance needs, with the machine learning model learning from historical data to predict optimal update moments without requiring complex manual configuration
Solution Approach 2:
The system employs self-service mechanisms where the database automatically monitors its own state, identifies when quantitative data becomes stale, and triggers updates without external intervention. The machine learning model autonomously learns patterns from operational data and makes decisions about update timing, reducing the need for complex external management systems while maintaining real-time adaptability
3Reliability
If quantitative data is updated during executing applications, then data freshness improves, but application performance is impacted
Solution Approach 1:
The patent implements preliminary action by proactively updating quantitative data during periods of low workload or predicted idle times before performance degradation occurs. The machine learning model predicts future data staleness and schedules updates in advance during optimal windows, ensuring data freshness is maintained while avoiding interference with active query execution and application performance
Data Source
AI summary
Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data.