Edge Data Management Agent for Intent Classification
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Solution Overview
Problem
Existing data management systems struggle to efficiently manage vast amounts of data produced at the edge of device ecosystems, leading to unmanageable complexity and lack of visibility into data production, intent, and accessibility.
Innovation Solution
A distributed data management system that employs a data management agent to classify data intent using heuristic rules and machine learning classifiers, assign global names, and perform data preparation actions to ensure data accessibility and appropriate services are applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored locally on edge devices, then data accessibility is improved, but device complexity and data management complexity increase
Solution Approach 1:
A data management agent is introduced as an intermediary component that mediates between applications and data storage systems. The agent automatically performs data classification, intent determination, and preparation actions, thereby reducing the complexity burden on edge devices while maintaining reliable data accessibility through centralized coordination.
Solution Approach 2:
The data management agent implements self-service mechanisms by autonomously classifying data, determining its intent, and performing necessary preparation actions without requiring manual intervention or complex application-level logic. This automation reduces device complexity while ensuring data is properly prepared and accessible when needed.
2Loss of information
If data classification and intent determination are performed manually, then data visibility is improved, but loss of time and productivity decrease
Solution Approach 1:
The data management agent autonomously performs data classification and intent determination using automated algorithms and machine learning models. This self-service approach provides comprehensive data visibility through automatic metadata generation and classification, while eliminating the time loss associated with manual data assessment and preparation.
Solution Approach 2:
Manual mechanical processes of data classification and intent determination are replaced with automated computational systems. The agent uses algorithmic processing and machine learning to automatically classify data and determine intent, substituting human manual work with efficient automated mechanisms that provide both visibility and speed.
3Productivity
If data preparation actions are performed automatically, then productivity is improved, but device complexity increases
Solution Approach 1:
The data management agent serves as an intermediary that centralizes automated data preparation functions. By consolidating classification, intent determination, and preparation actions in a single agent component, the system achieves high productivity through automation while managing complexity through centralized rather than distributed implementation.
Data Source
AI summary
Techniques described herein relate to a method for distributed data management. The method may include obtaining, by a data management agent of a data host and from an application executing on the data host, a request to access data; obtaining, by the data management agent, an information set associated with the data; making a determination, by the data management agent, that at least a portion of the data is not ready to be used by the application; and performing, by the data management agent and based on the determination, a data preparation action set.