Data Lineage Metrics for Multi-Hop Processing Traceability
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Solution Overview
Problem
Existing systems lack efficient methods to integrate and manage data lineage across multiple incompatible data structures, leading to inefficient processing, excessive resource consumption, and potential errors due to subjective determinations, which worsens with increasing data volumes.
Innovation Solution
A system generates data lineage metrics and overall metrics based on these, enabling efficient and error-free operations by integrating data lineage information from multiple sources, conserving computing resources and reducing delays through automated processing actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data lineage information is maintained in a centralized system for multiple datasets, then data traceability and audit capability are improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent segments data lineage information into individual hop-level metrics, where each transformation step is evaluated separately. This allows the system to manage complex data lineages by breaking them down into manageable units (individual hops) that can be processed and stored independently, reducing overall system complexity while maintaining complete traceability.
Solution Approach 2:
The patent transforms qualitative data lineage information into quantitative metrics by assigning numerical values to different aspects of data transformations (e.g., data quality scores, transformation complexity ratings). This parameterization enables automated processing and comparison of data lineage information, reducing manual intervention requirements and system complexity.
2Reliability
If comprehensive data lineage tracking is implemented across multiple data processing processes, then error tracing capability is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary evaluation of data lineage at each hop during the data processing workflow itself, rather than conducting comprehensive analysis after data has moved through all processes. By evaluating metrics incrementally at each transformation step, the system maintains error tracing capability while avoiding the computational burden of retrospective analysis across entire data lineages.
Solution Approach 2:
The patent enables selective skipping of hops in the data lineage evaluation process. When evaluating data lineage metrics, the system can bypass hops that have already been evaluated or hops with known good quality metrics, allowing faster processing while maintaining comprehensive error tracing capability for critical transformation steps.
3Device complexity
If subjective determinations are used for data lineage evaluation, then system simplicity is maintained, but processing accuracy and reliability deteriorate
Solution Approach 1:
The patent implements automated feedback mechanisms where data lineage metrics are continuously evaluated and used to adjust processing decisions. The system automatically feeds evaluation results back into the data processing workflow, enabling objective, data-driven decisions about data quality and processing priorities without requiring complex manual evaluation frameworks.
Solution Approach 2:
The patent enables data lineage information to evaluate itself through automated metric generation and comparison. The system automatically extracts, measures, and evaluates data lineage metrics without requiring external subjective assessment, achieving high evaluation accuracy through self-service automation while keeping the system architecture relatively simple.
4Quantity of substance
If data volumes increase, then data processing capability is improved, but resource consumption and error potential increase due to existing management methods
Solution Approach 1:
The patent applies partial evaluation by focusing metric generation on critical hops and data transformations rather than uniformly evaluating all data processing steps. This selective approach allows the system to handle large data volumes effectively by concentrating computational resources on the most impactful transformations, reducing overall resource consumption while maintaining processing capability.
Data Source
AI summary
In some implementations, a device may receive, by device, information identifying a data lineage for a plurality of datasets, the data lineage including, for a dataset of the plurality of datasets, information identifying one or more hops associated with the dataset, each hop, of the one or more hops, corresponding to a transformation of the dataset corresponding to a data processing process. The device may generate a plurality of data lineage metrics for a plurality of hops associated with a plurality of data processing processes to which the plurality of datasets is subjected in association with the data lineage. The device may generate an overall data lineage metric based on the plurality of data lineage metrics, the overall data lineage metric having a plurality of components. The device may transmit an output associated with the overall data lineage metric.