Resource Dependency Graph for Data Pipeline Navigation
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Solution Overview
Problem
Existing data retrieval methods, such as hierarchical navigation and query-based searching, are inefficient and impractical, especially in data pipeline systems where tracking resource dependencies is cumbersome and difficult, leading to challenges in maintaining data accuracy and adherence to protocols.
Innovation Solution
Implementing a resource dependency system that allows users to navigate and interact with resources based on their dependencies, using a toolbar or graph to display relationships, metadata, and enable filtering, editing, and updating, facilitating efficient tracking and verification of data transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hierarchical navigation or query-based searching is used to retrieve data, then data can be accessed through virtual folder hierarchies or search results, but the retrieval process becomes slow and inefficient as data volumes increase
Solution Approach 1:
The patent introduces a data dependency graph as an intermediary structure that visually represents relationships between datasets. This graph serves as a mediator between traditional navigation methods and the actual data, allowing users to understand data relationships and locate needed information more efficiently without traversing deep folder hierarchies or sifting through search results.
Solution Approach 2:
The patent adds a visual dimension to data navigation by creating a graphical representation of data dependencies. Instead of linear folder navigation or text-based search, users can visually traverse the dependency graph, adding a spatial dimension to data retrieval that makes relationships apparent and reduces the time needed to locate and understand data connections.
2Adaptability or versatility
If data is transformed, modified, and combined into new datasets, then data processing capabilities are enhanced, but tracking the original basis of each portion of data becomes increasingly difficult
Solution Approach 1:
The data dependency graph provides continuous feedback about data origins and relationships. As data is transformed and combined, the graph automatically updates to reflect new dependencies, maintaining an accurate record of data provenance. This feedback mechanism ensures that users can always trace back to original data sources regardless of how many transformations have been applied.
Solution Approach 2:
The system establishes data dependency relationships in advance, before transformations occur. By pre-mapping the dependency graph based on metadata and data relationships, the system creates a roadmap of data origins that persists through subsequent transformations, making tracking straightforward without requiring complex retrospective analysis.
3Ease of manufacture
If traditional file-retrieval structures are used, then data can be stored in virtual folders, but determining where data originated and verifying data accuracy becomes cumbersome
Solution Approach 1:
The dependency graph acts as an intermediary layer between the simple virtual folder storage structure and the complex task of tracing data origins. This intermediary visual representation makes it easy to see where data came from and how it relates to other datasets, without requiring users to manually navigate through folder hierarchies or analyze metadata.
Solution Approach 2:
The patent uses visual indicators such as color coding in the dependency graph to represent different data sources, transformation stages, or dependency types. These visual changes make it immediately apparent where data originated and how it has been processed, eliminating the need for detailed inspection of file metadata or folder paths.
4Reliability
If resource dependency relationships are tracked in data pipeline systems, then data accuracy can be maintained, but the complexity of managing and verifying dependencies increases
Solution Approach 1:
The patent segments the complex dependency management task into visual components within the dependency graph. Each node represents a dataset, and edges represent dependencies, breaking down the monolithic complexity into manageable visual elements. This segmentation allows users to focus on specific data relationships without being overwhelmed by the entire system's complexity.
Solution Approach 2:
The dependency graph serves as an intermediary management layer that simplifies verification of data relationships. Instead of manually checking each dependency connection, users can visually inspect the graph, which automatically maintains the correctness of relationships based on metadata and data transformations, reducing the cognitive load and complexity of dependency verification.
Data Source
AI summary
A resource dependency system dynamically determines and generates for display a minimized and collapsed resource dependency toolbar using two or more indicators to display a summarized view of dependency relationships to one or more selected items. For example, the system can analyze a resource dependency graph and determine root items, or items that do not depend on other items but are depended on by a selected item. The system can also determine leaf items, which no other items depend on. The system can also determine intermediary items that depend on root items and/or leaf items. Then, based on preconfigured instructions, the system can group the root, leaf, and intermediary items into two or more indicators and display the indicators on a graphical user interface conveying information about the selected item and how it is related to other items.