Graph Metadata Representation for Complex Data Relationship Retrieval
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Solution Overview
Problem
Existing technologies struggle to efficiently process and visualize metadata from diverse data sources, lacking a unified representation that captures complex relationships among data files, which hinders effective data product development and retrieval.
Innovation Solution
A graph database is generated to represent metadata, comprising nodes for features and edges for relationships, allowing for the visualization and processing of metadata from various sources in a unified manner, with natural language processing to extract patterns and derive new insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If metadata from diverse data sources is processed using traditional methods, then data processing can be performed, but efficient processing and visualization of metadata relationships cannot be achieved
Solution Approach 1:
The patent introduces a graph database as an intermediary layer between diverse data sources and the processing system. This graph database converts heterogeneous metadata from multiple data sources into a unified graph structure with standardized nodes and edges, enabling efficient processing while simplifying relationship visualization. The graph database acts as a mediator that transforms complex, diverse metadata into a consistent format that can be easily processed and visualized.
Solution Approach 2:
The patent changes the structural parameters of metadata representation by transforming traditional flat or hierarchical metadata formats into a graph-based structure. This parameter change involves converting metadata attributes into graph nodes and relationships into graph edges, fundamentally altering how metadata is organized and processed. This structural transformation enables more efficient querying and visualization of complex relationships while maintaining processing efficiency.
2Loss of information
If complex relationships among data files are represented in traditional formats, then data can be stored, but complex relationships cannot be visually represented and insights cannot be provided
Solution Approach 1:
The patent adds a visual dimension to metadata representation by mapping graph database structures to graphical visualizations. Nodes representing data files, attributes, and other entities are displayed as visual elements in a graphical interface, with edges representing relationships drawn as connecting lines. This dimensional transformation from abstract data structures to visual representations enables users to easily perceive complex relationships and gain insights that would be difficult to obtain from traditional tabular or hierarchical formats.
Solution Approach 2:
The patent employs color coding to enhance the visualization of metadata relationships. Different node types (data files, attributes, data sources) are represented by different colors, and relationship types are indicated through color-coded edges or connection styles. This visual encoding system enables users to quickly distinguish between different entities and relationships, making complex metadata structures easily interpretable and providing immediate visual insights into data relationships.
3Adaptability or versatility
If metadata from multiple data sources is unified, then diverse data can be processed, but redundancy cannot be removed and patterns cannot be extracted
Solution Approach 1:
The patent performs preliminary processing of metadata before full integration into the graph database. During the metadata ingestion phase, the system pre-processes incoming metadata to identify and remove obvious duplicates, standardize attribute names and formats, and validate data structures. This preliminary action reduces the burden on subsequent processing steps and prevents redundancy from propagating through the system, while still maintaining the ability to unify diverse metadata sources.
Solution Approach 2:
The patent implements feedback mechanisms that analyze the graph database structure and identify redundant or inconsistent metadata entries. The system continuously queries the graph database to detect duplicate relationships, conflicting attribute definitions, or inconsistent data patterns across different data sources. When redundancy is detected, the system provides feedback to automatically resolve conflicts, merge duplicate entries, or flag issues for manual review, thereby maintaining data quality while unifying diverse metadata sources.
Data Source
AI summary
This disclosure relates to representing and using metadata via graph database. In some aspects, a method includes receiving, at one or more computing devices, first metadata associated with data files from one or more data sources, the first metadata representing a plurality of features of associated data included in the data files, the plurality of features including at least one of a file name, a table name, an attribute, a row name, and a column name; determining relationships among the plurality of features to generate second metadata representing content of the data files; and generating a graph database representing the content of the data files, the graph database including a set of nodes and a set of edges, wherein each node in the set of nodes represents a feature of the plurality of features, and each edge represents a relationship between two nodes in the set of nodes.