Dynamic Reference Database for Video Content Matching
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Solution Overview
Problem
Media systems face challenges in managing system resources for identifying and matching unknown content due to the need for large and accurate reference databases, which is inefficient and resource-intensive.
Innovation Solution
A method for improving system resource management by determining the retention and deletion of reference data sets based on the popularity of video segments, using algorithms that consider viewer numbers, ratings, and social media metrics to optimize data storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large reference data set is maintained to improve content matching accuracy, then the reliability of content identification is improved, but the system resource utilization deteriorates
Solution Approach 1:
The reference database is made dynamic by automatically adding new content references and removing outdated ones based on popularity metrics. The system continuously updates the reference data set by monitoring content popularity through algorithms that analyze viewer numbers, ratings, and social media metrics, ensuring the database adapts to changing content consumption patterns while maintaining optimal size.
Solution Approach 2:
The system selectively discards less popular content references from the reference database while preserving popular content. By using popularity-based algorithms to identify and remove outdated or unpopular content references, the system recovers system resources while maintaining the ability to accurately identify currently relevant content, thus resolving the contradiction between database size and resource utilization.
2Productivity
If the reference data set is reduced to improve system resource utilization, then the productivity is improved, but the reliability of content matching deteriorates
Solution Approach 1:
The reference database is optimized by maintaining high-quality, popular content references with greater detail while using more compact representations for less popular content. The system applies different storage and retention strategies to different segments of the reference data based on their popularity metrics, ensuring that resources are allocated efficiently while preserving matching accuracy for the most relevant content.
3Reliability
If reference content is retained for longer periods to improve content identification reliability, then the reliability is improved, but the loss of time for system maintenance increases
Solution Approach 1:
The system implements periodic updates to the reference database by automatically adding new popular content and removing outdated content at scheduled intervals. This periodic maintenance approach, driven by popularity metric analysis, ensures the reference database remains current and reliable while minimizing the time and resources required for manual maintenance, as the process is automated and occurs in systematic cycles.
Data Source
AI summary
Provided are devices, computer-program products, and methods for improved management of system resources in a matching system. For example, examples can increase the efficiency of system resource utilization by managing the duration that data related to video segments are retained based on data that takes into account an identified popularity of a video segment. The identified popularity can be determined by algorithms that take into account numbers of viewers who watched the video segment, ratings of the video segment, metrics derived from remote sources, or any other factor that can indicate likelihood that the video segment will be viewed.