Pattern-Based Logical Entities for Data Relationship Discovery
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Solution Overview
Problem
Enterprises face challenges in organizing and discovering relationships between large volumes of data, including identifying a singular view of data entities and understanding their relevance, which conventional systems fail to address effectively.
Innovation Solution
A method and system utilizing patterns and logical entities to identify and maintain relationships between data assets by generating labels that represent sets of data assets, allowing users to filter and view only relevant data subsets through predefined and user-defined patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If enterprises collect and handle large amounts of data from various sources, then the quantity and variety of data assets increase, but the difficulty of organizing and discovering relationships between data assets increases
Solution Approach 1:
The patent introduces logical entities as intermediary objects that mediate between raw data assets and user queries. These logical entities serve as abstract representations that capture relationships between data assets without requiring direct analysis of the underlying data structures. The system automatically generates logical entities from data assets, creating an intermediate layer that simplifies relationship discovery and data organization.
Solution Approach 2:
The patent creates simplified copies of data assets in the form of logical entities. Instead of working directly with complex data structures and relationships, the system generates label-based representations that capture essential characteristics and relationships. These logical entity copies enable efficient querying and analysis without requiring direct manipulation of the underlying data assets.
2Quantity of substance
If conventional systems attempt to organize large volumes of data, then data coverage increases, but the ability to identify singular views and understand relevance deteriorates
Solution Approach 1:
The patent segments data assets into distinct logical entities based on their characteristics and relationships. Each logical entity represents a specific aspect or view of the data, allowing users to explore different perspectives without losing the broader data context. This segmentation enables the system to maintain multiple organized views of the same data assets simultaneously.
Solution Approach 2:
The patent creates logical entities that serve multiple functions: they represent data assets, capture relationships between assets, enable filtering and searching, and provide contextual information. Each logical entity acts as a universal representation that can be used across different query types and analysis scenarios, maintaining relevance information across diverse data operations.
3Quantity of substance
If users need to view and operate on complete data sets, then data completeness is maintained, but usability and understanding of data relationships deteriorates
Solution Approach 1:
The patent extracts essential relationship and characteristic information from complete data sets and encapsulates it in logical entities. Users can work with these extracted representations to understand data relationships without being overwhelmed by the full complexity of the underlying data. The complete data sets remain intact in the background, ensuring data completeness while the extracted logical entities provide usability.
Data Source
AI summary
Systems, devices, and methods discussed herein are directed to utilizing patterns and logical entities to identify and maintain relationships between data assets. In some embodiments, a query comprising a logical entity qualifier, one or more pattern identifiers that indicate a pattern, and a data set identifier may be received. The pattern is executed against a data set corresponding to the data set identifier and one or more logical entities are generated based on this execution. A logical entity may be a label that represents a set of one or more data assets in a data set. Assets that share a label can share attributes that are described by the label. The label corresponding to each logical entity may be presented, where each label represents a different set of data assets which share a common trait. In some embodiments, the user may define a pattern by which commonality may be assessed.