Dynamic Data Retention via Interestingness Scoring
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Solution Overview
Problem
Conventional data repository maintenance tools are inefficient as they rely on explicit rules for data retention and removal, making them inflexible and costly to customize for specific applications, and fail to differentiate between data based on its interestingness relative to the application's purpose.
Innovation Solution
A system and method that computes an interestingness value for data records based on the application's goal, allowing for dynamic data retention and removal by comparing these values against a threshold, thereby supporting real-time data management and reducing database size without requiring extensive hardware upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional explicit rules are used for data retention and removal, then data maintenance can be performed, but the system becomes inflexible and costly to customize for specific applications
Solution Approach 1:
The patent changes the parameter of data evaluation from static explicit rules to dynamic interestingness scores. By computing interestingness values based on application goals and comparing them against thresholds, the system adapts to different applications without requiring custom tool development, resolving the contradiction between adaptability and complexity
Solution Approach 2:
The system enables self-service by automatically computing interestingness scores and performing retention/removal decisions based on application-specific goals. This eliminates the need for manual customization of maintenance tools for each application, achieving adaptability without proportional increases in complexity
2Ease of operation
If all data is retained except data older than a fixed threshold, then data maintenance is simple, but the system treats all data the same and lacks flexibility
Solution Approach 1:
The patent applies local quality by assigning different interestingness scores to different data records based on their specific characteristics and relevance to application goals. Instead of uniform treatment, each data record receives localized evaluation, enabling flexible differentiation while maintaining automated operation
Solution Approach 2:
The system introduces dynamics by making the retention policy adaptive rather than static. Interestingness scores and thresholds can be adjusted based on changing application requirements, providing flexibility while keeping the operational mechanism simple through automated computation
3Quantity of substance
If data repository size is reduced by removing irrelevant data, then hardware requirements and costs decrease, but the system must efficiently identify and remove low-value data
Solution Approach 1:
The patent replaces manual or rule-based mechanical data evaluation with automated interestingness score computation. This substitution enables efficient identification and removal of low-value data at scale, reducing repository size while maintaining high productivity through systematic automated processing
4Reliability
If custom data maintenance tools are developed for each application, then data retention can be optimized, but the development process becomes challenging, time-consuming, and costly
Solution Approach 1:
The patent creates a universal data maintenance system that computes interestingness scores based on application goals. This multi-functional approach allows the same system to optimize data retention across different applications without requiring separate custom tool development, achieving reliability while eliminating time-consuming development cycles
Data Source
AI summary
A system includes a processor and a memory coupled with and readable by the processor and storing therein a set of instructions. When executed by the processor, the processor is caused to receive application events associated with an application and create data records based on the application events. The processor is further caused to compute an interestingness value for each of the data records based on a goal of the application, assign the computed interestingness value to each of the data records and store each of the data records with the assigned interestingness value.