Feature Graph Data Structure for Statistical Data Discovery
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Solution Overview
Problem
Conventional approaches struggle to identify and access data that is statistically associated with a topic of interest, making it difficult to construct effective training sets for machine learning models, and existing data organization methods are inefficient for predictive modeling and machine learning tasks.
Innovation Solution
The use of a Feature Graph data structure that represents nodes and edges to depict statistical associations between variables and topics, enabling efficient identification and access of relevant datasets for training models, and providing tools for users to understand and navigate these relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is organized using conventional approaches (tables, rows, columns), then data storage and basic access are simplified, but the ability to identify and access data that is statistically associated with a topic of interest deteriorates
Solution Approach 1:
The patent segments data into two distinct representations: traditional tabular data for storage and a knowledge graph for semantic relationships. This allows the system to maintain simple data storage while adding sophisticated statistical association capabilities through the knowledge graph structure that maps variables, concepts, and their statistical relationships.
Solution Approach 2:
The knowledge graph acts as an intermediary layer between raw data and user queries. It mediates the connection between conventional data storage and statistical association requirements by providing a structured representation that explicitly captures statistical relationships between variables and topics, enabling efficient retrieval of statistically associated data.
2Ease of manufacture
If conventional data organization methods are used, then data storage is straightforward, but constructing effective training sets for machine learning models becomes difficult
Solution Approach 1:
The system performs preliminary action by pre-building the knowledge graph structure that maps statistical associations between variables and topics before training models. This pre-organized semantic structure enables rapid identification and retrieval of relevant training data without requiring manual construction or complex querying during the training process.
Solution Approach 2:
The patent replaces manual or conventional mechanical data organization methods with an automated knowledge graph system that computationally identifies and organizes statistical associations. This substitution of mechanical data handling with intelligent semantic mapping significantly improves training set construction efficiency while maintaining organizational simplicity.
3Ease of operation
If semantic matching of words with labels is used for data discovery, then finding data about a topic is improved, but discovering data about variables statistically associated with the topic deteriorates
Solution Approach 1:
The patent adds another dimension to data organization by introducing a knowledge graph layer that captures statistical relationships between variables and topics. This dimensional expansion allows the system to represent not only semantic relationships but also statistical associations, enabling users to discover both topic-related data and statistically associated variables simultaneously.
Solution Approach 2:
The knowledge graph structure serves multiple functions: it represents semantic relationships between concepts, captures statistical associations between variables and topics, and enables both topic-based discovery and statistical association querying. This multi-functionality allows a single system to address both semantic search and statistical data discovery requirements.
Data Source
AI summary
A system and associated methods for organizing, representing, finding, discovering, and using data. Embodiments represent information and data in the form of a data structure termed a Feature Graph, that includes nodes and edges, where the edges serve to connect a node to one or more other nodes. A node in a Feature Graph may represent a variable, such as a measurable object, characteristic, or factor of a study. An edge in a Feature Graph may represent a measure of a statistical association between a node and one or more other nodes. Datasets that demonstrate or support the statistical association or measure the associated variable may be accessed through an identifier in a Feature Graph. An application may traverse a Feature Graph and aggregate and process data associated with a set of nodes or edges.