Pre-fetch Engine Data Expiration for Mesh Network Latency
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Solution Overview
Problem
Large enterprise organizations face inefficiencies in data retrieval due to latency and compliance challenges when accessing data from multiple geographic locations, each with different regulatory requirements.
Innovation Solution
A pre-fetch engine utilizing a machine learning model generates a pre-fetch template to identify responsive data sets, which are then retrieved from data repositories located in various geographic regions, with data controllers ensuring compliance with local regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is retrieved from multiple geographic locations, then data availability is improved, but latency increases
Solution Approach 1:
The pre-fetch engine proactively retrieves data from multiple geographic locations before it is actually requested. By anticipating data needs and fetching data in advance, the system improves data availability while preventing latency issues, as the data is already available when needed for processing.
Solution Approach 2:
The system divides data retrieval operations into separate pre-fetching and processing phases. The pre-fetch engine segments the data retrieval task from the main data processing workflow, allowing data to be fetched asynchronously from multiple locations without blocking the processing pipeline, thereby reducing overall latency.
2Speed
If data is pre-fetched from multiple sources, then data retrieval speed is improved, but data compliance with local regulations becomes more difficult to ensure
Solution Approach 1:
The system applies different compliance rules and data handling procedures to data from different geographic locations. Each data source has associated local regulations that are enforced specifically for that source's data, allowing the system to maintain high retrieval speed while ensuring location-specific compliance requirements are met through customized processing paths.
Solution Approach 2:
The pre-fetch engine acts as an intermediary layer between multiple data sources and the data processing system. It manages compliance complexity by implementing a centralized evaluation mechanism that assesses data against local regulations before data enters the main processing pipeline, thereby simplifying compliance management while maintaining fast retrieval speeds.
3Measurement precision
If a machine learning model is used to generate pre-fetch templates, then data identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The machine learning model enables the pre-fetch engine to automatically identify and prioritize data sets without requiring manual configuration or intervention. The system self-optimizes its data selection process by learning from patterns in data requests and retrieval outcomes, improving identification accuracy while the automated nature of the process prevents excessive complexity from accumulating.
Data Source
AI summary
Arrangements for controlling data retrieval are provided. In some aspects, a data request may be received by a computing platform. A pre-fetch engine may be executed to analyze the data request and generate, using a machine learning model, a pre-fetch template identifying data sets responsive to the request. The pre-fetch template may be transmitted to one or more data repositories and response data sets including data use parameters may be received. The received plurality of data sets may be analyzed to determine whether a triggering action has occurred. If a triggering action has occurred, one or more data modification functions may be performed. For instance, data may be deleted, prevented from further processing, or the like, in response to detection of a triggering action.