Dynamic Data Management Tuning via Link Preference Detection
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Solution Overview
Problem
Conventional data management systems rely on static algorithms for record matching, requiring users to submit multiple queries and consume more resources and time to achieve desired search results, often leading to 'no result found' responses and inefficient user interactions.
Innovation Solution
The system automatically configures data management algorithms by analyzing user interaction data using machine learning models to identify usage patterns, adjusting attribute weights, thresholds, and adding new entities, thereby improving search results and reducing user effort and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static algorithms are used for record matching, then system simplicity is maintained, but search efficiency and user experience deteriorate
Solution Approach 1:
The patent applies dynamics by transitioning from static matching algorithms to dynamic algorithms that automatically adapt based on user interactions. The system continuously learns from user behavior patterns and adjusts matching criteria in real-time, making the algorithm flexible and responsive to changing user needs without manual reconfiguration.
Solution Approach 2:
The system implements self-service through automatic algorithm tuning based on user interaction data. Instead of requiring manual configuration or expert intervention, the system autonomously analyzes user behavior patterns and self-adjusts matching parameters, reducing the need for human expertise while improving search efficiency.
2Measurement precision
If multiple queries are submitted manually, then search precision can be improved, but user time and computational resources increase
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring user interactions with search results and using this feedback to refine matching algorithms. User behaviors such as clicking, viewing, or ignoring results are fed back into the system to automatically adjust matching criteria, improving precision over time without requiring additional manual queries.
Solution Approach 2:
The system performs preliminary action by pre-tuning algorithms based on aggregate user behavior data before individual users interact with the system. This preliminary learning from collective user patterns enables faster and more accurate search results from the outset, reducing the need for users to submit multiple queries to achieve desired precision.
3Adaptability or versatility
If manual configuration is used, then system adaptability is maintained, but operational complexity and time consumption increase
Solution Approach 1:
The system achieves self-service operational ease by automatically adapting to different user needs and data characteristics without manual configuration. The algorithm continuously learns from user interactions and self-adjusts parameters such as matching thresholds and weightings, maintaining high adaptability while eliminating the operational complexity of manual tuning.
Solution Approach 2:
The patent applies parameter changes by automatically modifying algorithm parameters such as matching thresholds, attribute weights, and search criteria based on detected user behavior patterns. This dynamic parameter adjustment enables the system to adapt to different user needs and data characteristics automatically, maintaining versatility without increasing operational complexity.
Data Source
AI summary
Configuring a data management system by receiving user interaction data associated with search results associated with a first system configuration, identifying a usage pattern in the user interaction data using a first machine learning model, and altering the first system configuration according to the usage pattern.