IoT Data Maturity Engine for Real-Time Validation
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Solution Overview
Problem
The increasing volume of data from IoT devices poses challenges in managing data maturity, with existing technologies failing to efficiently validate and process data in real-time, leading to potential inaccuracies and inefficiencies in data management.
Innovation Solution
A data maturity engine dynamically analyzes metadata from IoT devices, considering factors like timestamps, source reputation, and intended use to evaluate data maturity, enabling informed data management operations such as discarding, temporary storage, or immediate processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all IoT data is processed immediately without validation, then processing speed is maintained, but data reliability and accuracy deteriorate
Solution Approach 1:
The patent applies preliminary action by evaluating data maturity factors (completeness, quality, timeliness, relevance) before data processing operations. The system assesses whether data meets required standards for processing, validation, enrichment, or archival based on its maturity level, preventing premature processing of immature data while maintaining efficient workflows.
2Reliability
If data maturity evaluation is performed for all IoT data, then data quality improves, but system complexity increases
Solution Approach 1:
The patent segments the data evaluation process into distinct maturity factors (completeness, quality, timeliness, relevance) that can be independently assessed. This segmentation allows the system to evaluate specific aspects of data maturity without requiring complex holistic analysis, simplifying the overall evaluation mechanism while maintaining comprehensive data quality assessment.
Solution Approach 2:
The patent uses parameter changes by defining quantifiable metrics for each maturity factor (e.g., timestamp recency for timeliness, data format validity for quality). These parameter-based evaluations transform subjective data quality assessment into objective, computable metrics that simplify the evaluation process while improving data quality determination accuracy.
3Productivity
If immature data is processed, then processing throughput is maintained, but data utility and accuracy worsen
Solution Approach 1:
The patent implements feedback by continuously monitoring data maturity factors and using this information to dynamically determine appropriate processing operations. The system provides feedback loops where data evaluation results inform subsequent processing decisions, ensuring that only data meeting maturity thresholds undergoes processing, thereby maintaining both throughput and utility through adaptive control.
Data Source
AI summary
Disclosed aspects relate to Internet of Things (IoT) data maturity management. A set of IoT data may be ingested by a data maturity engine. A set of maturity factors which indicates a fitness for utilization of the set of IoT data may be determined. The determining may occur with respect to the set of IoT data by the data maturity engine. A data management operation may be identified by the data maturity engine based on the set of maturity factors for the set of IoT data. The data management operation may be identified to manage the set of IoT data. The data management operation may be carried-out to manage the set of IoT data.