Edge Data Loss Mitigation via ML and Distributed Ledger
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Solution Overview
Problem
In edge computing environments, data loss occurs due to the accumulation of dark data, which is not utilized during transaction processing, leading to storage of both critical and redundant data elements, necessitating a system to analyze and mitigate this loss.
Innovation Solution
A system utilizing machine learning to retrieve representation information from dark data and leveraging distributed ledger technology to track transactions, reconcile duplicate data elements, and generate identification tags for registration on a distributed ledger, thereby addressing data loss and optimizing data utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If edge computing nodes store all data elements including dark data, then data retention is improved, but data loss increases due to accumulation of unused data
Solution Approach 1:
The system extracts valuable information from dark data by applying machine learning algorithms to retrieve representation information. This extraction process separates useful insights from the accumulated dark data, converting it into actionable knowledge while removing the harmful accumulation of unused data elements.
Solution Approach 2:
The system changes the state of dark data by transforming it into representation information through machine learning processing. This parameter transformation converts unusable dark data into valuable representation information that can be stored on the distributed ledger, thereby changing the quality and utility of the data.
2Productivity
If machine learning algorithms process dark data, then data utilization is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by retrieving representation information from dark data before final transaction completion. This preliminary action allows the machine learning algorithms to process and extract valuable information in advance, reducing the impact on overall transaction processing time.
Solution Approach 2:
The system applies machine learning algorithms selectively to dark data that contains potential value, rather than processing all data uniformly. This partial action approach focuses computational resources on the most promising dark data elements, optimizing the balance between data utilization and processing time.
3Reliability
If distributed ledger technology tracks all transactions, then data integrity is improved, but system complexity increases
Solution Approach 1:
The system creates representation information copies of dark data and stores them on the distributed ledger instead of tracking all raw transaction data. This copying approach maintains data integrity through distributed verification while reducing the complexity of processing and managing the full volume of transaction data.
Solution Approach 2:
The system segments data tracking into two layers: raw transaction data processed by edge computing nodes and representation information stored on the distributed ledger. This segmentation allows the distributed ledger to focus on verifying and tracking essential transaction outcomes without the complexity of processing all underlying data elements.
Data Source
AI summary
Systems, computer program products, and methods are described herein for mitigating data loss in an edge computing environment using machine learning and distributed ledger techniques. The present invention is configured to receive an indication that one or more edge computing nodes is processing one or more portions of a transaction; retrieve dark data associated with each of the one or more edge computing nodes; initiate a machine learning algorithm on the dark data retrieved from each of the one or more edge computing nodes; capture, using the machine learning algorithm, representation information for each of the one or more edge computing nodes from their respective dark data; generate a ledger record for the representation information for each of the one or more edge computing nodes; and register the ledger record for the representation information for each of the one or more edge computing nodes on a first distributed ledger.