Dataflow Lineage Control via Embedded Version Tracking
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Solution Overview
Problem
Large organizations face challenges in detecting data anomalies, preventing data loss, and maintaining data quality due to the complexity of their IT infrastructure, leading to difficulties in ensuring data integrity and accuracy across various databases and systems.
Innovation Solution
A dataflow control architecture that embeds lineage information within data itself, using a control value to track data transformations and integrity across databases, with a lineage server aggregating and inferring dataflow graphs to ensure real-time data integrity and quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual reconciliation and validation methods are used to ensure data integrity, then data quality control is achieved, but time consumption and computing resources are excessively high
Solution Approach 1:
The patent applies preliminary action by embedding lineage control values and version control values into data elements as they are created or transformed, rather than performing validation afterward. The system proactively tracks data provenance and transformation history through control values that are generated and attached during data flow, enabling automated integrity verification without manual reconciliation at later stages.
2Productivity
If automated data lineage tracking is implemented, then data quality control efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces control values as intermediary elements that mediate between data elements and the lineage tracking system. These control values serve as compact carriers of metadata information (provenance, transformations, quality attributes) that can be processed automatically without requiring complex analysis of the actual data content or transformation logic, thereby simplifying the tracking infrastructure.
3Loss of information
If comprehensive data tracking is implemented across all databases and systems, then data provenance visibility is improved, but computational overhead increases
Solution Approach 1:
The patent extracts essential lineage information into separate control values that are attached to data elements, rather than storing comprehensive tracking information within the data structures themselves or requiring continuous monitoring of all data operations. This extraction approach allows the system to track only the critical provenance attributes needed for quality control, reducing computational overhead while maintaining visibility.
4Manufacturing precision
If real-time data validation is performed, then data accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent performs validation准备工作 in advance by embedding control values with provenance and quality information during data creation and transformation. When validation is needed, the system can quickly retrieve and verify pre-computed control values rather than performing complex validation calculations in real-time, thus maintaining data accuracy without significantly impacting processing speed.
Data Source
AI summary
A system for aggregating dataflow lineage information is disclosed. The system receives one or more input data elements and determines a dataflow path for the one or more input data elements. The dataflow path includes at least a data storage node and a computation node. Then, the system identifies a lineage control value associated with the data storage node and a version control value associated with the computation node. The system generates an output lineage for the one or more input data elements by appending the lineage control value to the version control value.