Dataflow Control Architecture with Embedded Lineage for Latency Tracking
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Solution Overview
Problem
Large organizations face challenges in maintaining data integrity and quality due to complex IT systems, where data undergoes multiple transformations, making it difficult to track and validate data accuracy, timeliness, and completeness across various databases.
Innovation Solution
A dataflow control architecture that embeds lineage information within data as control values, allowing a lineage server to aggregate and track data flows, generate dataflow graphs, and perform real-time integrity checks, ensuring data accuracy, timeliness, and completeness by associating lineage and version control values with each data element.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual reconciliation and validation operations are performed across multiple databases, then data integrity and quality can be ensured, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing control values (checksums, hashes, or other validation data) alongside the actual data in each database table. These control values are generated in advance using predetermined algorithms and stored in dedicated control value tables. When data flows between databases, the control values travel with the data, enabling immediate validation without requiring time-consuming manual reconciliation operations at the point of data receipt.
Solution Approach 2:
The patent introduces control values as intermediary elements that mediate between data production and data consumption processes. These control values act as trusted carriers that accompany data through the entire dataflow path, enabling receiving systems to independently validate data integrity without contacting the source system. The control values serve as self-contained proof of data authenticity and completeness, eliminating the need for complex inter-system validation protocols.
2Reliability
If comprehensive data validation and lineage tracking are implemented across complex IT systems, then data quality control improves, but system complexity and computational overhead increase
Solution Approach 1:
The patent segments the data validation function into independent, reusable components. Each database table has its own control value table with pre-computed validation data. The validation logic is divided into separate generation algorithms (stored in version control tables) that can be independently selected and applied to different data types. This segmentation allows the system to handle complex validation requirements through simple, modular operations at each dataflow stage, reducing overall system complexity.
Solution Approach 2:
The patent uses copying by creating and storing control values that are replicas or derivatives of the actual data. These control values (such as checksums, hashes, or simplified validation representations) are computed from the source data and copied alongside it through the dataflow. The receiving systems validate data by comparing against these copied control values rather than re-computing validation metrics from scratch, significantly reducing computational overhead while maintaining validation comprehensiveness.
3Productivity
If automated data lineage tracking with control values is implemented, then manual reconciliation efforts are reduced, but initial system setup and infrastructure requirements increase
Solution Approach 1:
The patent applies universality by designing a multi-functional control value infrastructure that serves multiple purposes simultaneously. The same control value tables and validation mechanisms handle data integrity verification, lineage tracking, version control, and anomaly detection across diverse data types and workflows. The version control tables store algorithms that can be selectively applied to different data generation processes, making the infrastructure adaptable to various operational requirements without requiring separate validation systems for each use case.
Data Source
AI summary
A system for performing a timeliness control is disclosed. The system identifies a dataflow path for performing timeliness control and identifies a first network node and a second network node of the dataflow path for determining a latency between the first and the second network node. The system determines an output lineage corresponding to the dataflow path and identifies, from the output lineage, a first control value associated with the first network node and a second control value associated with the second network node. Then, the system extracts a first timestamp from the first control value and a second timestamp from the second control value and determines the latency based on the first timestamp and the second timestamp. Although the intranode latency is described herein with respect to a first and second nodes, the intra-node latency can be determined for up to n nodes using the techniques described herein.