Multi-Cache Digital Output Generation for Low-Resource Analytics
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Solution Overview
Problem
Multimedia streaming services face challenges in accurately and reliably processing performance data associated with streamed content items without excessive memory or processor utilization, especially with increasing numbers of digital content providers and multimedia content items.
Innovation Solution
A multi-cache-based system that uses a machine learning model to preprocess and sort data, storing it in a first level cache and then transferring relevant information to a primary cache for efficient digital output generation, reducing processor and memory utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data processing methods are used to process performance data from multiple digital content providers, then processing accuracy and reliability can be maintained, but memory utilization and processor utilization become excessive
Solution Approach 1:
The patent divides the data processing system into multiple cache levels (first level cache, second level cache, and primary cache) that segment the processing of performance data. Each cache level handles specific portions of the data, allowing the system to process large volumes of performance data from multiple digital content providers while distributing memory usage across hierarchical storage layers, thus reducing overall memory utilization requirements while maintaining processing accuracy.
Solution Approach 2:
The patent introduces intermediary cache structures between the data sources and the final processing stage. The first level cache and second level cache act as intermediaries that buffer and pre-process performance data before it reaches the primary cache for final analysis. This intermediary approach allows accurate processing of performance metrics while reducing the memory burden on the main processing system.
2Reliability
If traditional data processing methods are used to process performance data from multiple digital content providers, then processing accuracy and reliability can be maintained, but processor utilization becomes excessive
Solution Approach 1:
The patent applies preliminary action by pre-processing performance data in the first level cache and second level cache before the data reaches the primary cache for final processing. Data is aggregated, filtered, and organized in advance at lower cache levels, which reduces the computational burden on the main processor when generating performance reports. This preliminary processing maintains accuracy while significantly reducing processor utilization during the critical reporting phase.
Solution Approach 2:
The multi-level cache architecture segments the processing workload across different storage and processing stages. Each cache level performs specific processing tasks, dividing the overall computational burden into manageable portions that can be handled with lower processor utilization while maintaining the reliability and accuracy of performance data processing.
3Productivity
If multi-cache based processing is used to reduce memory and processor utilization, then resource efficiency improves, but system complexity increases
Solution Approach 1:
While segmentation into multiple cache levels does increase structural complexity, it dramatically improves resource efficiency by enabling the system to process performance data from multiple digital content providers with reduced memory and processor utilization. The segmented architecture allows each cache level to be optimized for specific processing tasks, improving overall productivity despite the increased system complexity.
Data Source
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AI summary
Multi-cache-based digital output generation is provided. A system receives data objects that include fields from a remote data source. The system sorts the data objects based on a field to generate a sorted data set. The system cleans the sorted data set to generate a clean data set based on a policy. The system receives a request for a type of digital output based on the data objects received from the data source and loads a portion of the clean data set to a first level cache. The system selects a machine learning model configured for the type of digital output, and loads a primary cache with a subset of fields stored in the first level cache selected based on the machine learning model. The system generates, based on the first level cache being complete, digital output corresponding to the type of digital output from data in the primary cache.