Data Flow Error Lineage Tracing via Segmented Node Validation
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Solution Overview
Problem
Existing data visualization tools often fail to effectively identify and resolve errors in data flows, particularly in large or complex datasets, as they lack sufficient information to pinpoint the root cause of errors, which can be introduced at any step in the data transformation process.
Innovation Solution
A method and system that display a user interface with a flow diagram allowing users to specify validation rules, identify errors, and provide a lineage of errors through a data flow trace, enabling users to visually trace and resolve errors by displaying a natural language summary, error profile, and data flow trace, along with proposed solutions based on data values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data visualization tools are used to analyze large or complex datasets, then the ability to understand data distribution and trends is improved, but the difficulty of detecting and measuring errors increases
Solution Approach 1:
The patent segments the data flow into discrete nodes representing individual transformation steps. Each node can be independently examined for errors, and error lineage is traced through these segmented nodes rather than searching through the entire complex data flow at once. This segmentation enables precise error detection while maintaining manageability in large datasets.
Solution Approach 2:
The patent introduces an intermediary error lineage tracking mechanism that connects detected errors to their source nodes. This intermediary structure (the lineage graph) mediates between the complex data flow and the user, providing a simplified path to error sources without requiring direct analysis of the entire data transformation pipeline.
2Reliability
If traditional error identification methods are used in data flows, then some errors can be detected, but the loss of information about error origin and propagation occurs
Solution Approach 1:
The patent implements feedback by tracing error lineage backward from detected errors to their source nodes. The system provides feedback information about where errors originated, how they propagated through the data flow, and which specific nodes are responsible. This feedback loop eliminates information loss about error origins while maintaining reliable error detection.
Solution Approach 2:
The patent performs preliminary action by pre-establishing the lineage relationships between nodes in the data flow before errors occur. The system maintains a map of how data transforms through each node, so when an error is detected, the lineage information is already available without requiring additional investigation. This preliminary structuring prevents loss of error origin information.
3Productivity
If complex data transformation flows are created to handle large datasets, then the productivity of data analysis is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex data transformation flow into discrete, named nodes that represent specific transformation operations. This segmentation allows the system to maintain high productivity through complex transformations while managing complexity by breaking the flow into manageable, identifiable units that can be independently analyzed and debugged.
Solution Approach 2:
The patent introduces an intermediary lineage tracking system that mediates between the complex data flow structure and the user. This intermediary layer provides simplified error tracing without requiring changes to the underlying complex data transformation logic, thereby maintaining productivity while reducing the perceived complexity for users.
4Reliability
If data transformation steps are increased to improve data quality, then the reliability of output data is improved, but the time required to identify errors increases
Solution Approach 1:
The patent performs preliminary action by pre-establishing the lineage relationships between all nodes in the data flow before errors occur. This upfront structuring creates a ready-made error tracing path, so when errors are detected in high-quality multi-step transformations, the time to identify their origin is minimized because the propagation path is already mapped.
Solution Approach 2:
The patent implements feedback by automatically tracing error lineage from detection points back to source nodes through the transformation steps. This feedback mechanism eliminates the time penalty that would otherwise result from manually investigating each transformation step, allowing the system to maintain high reliability through multiple transformation steps without proportionally increasing error identification time.
Data Source
AI summary
A method displays a user interface (UI) that includes a flow diagram having a plurality of nodes, receives user specification of a validation rule for a first node of the plurality of nodes in the flow diagram, and determines that an intermediate data set violates the validation rule. In response to determining that the first intermediate data set violates the validation rule, the method identifies errors corresponding to rows in the intermediate data set, and displays an error resolution UI that provides information about the errors. The error resolution UI includes a data flow trace region providing lineage of the errors in the flow. When a user selects an error in the data flow trace region, the data flow trace region is updated to provide the lineage of the selected error, including an updated graphic depiction for the respective error at each visually represented node.