Feature Graph Data Structure for Statistical Variable Discovery
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Solution Overview
Problem
Conventional approaches to organizing and accessing data are inefficient for identifying and utilizing statistically associated variables, particularly in machine learning, as they rely on semantic relationships rather than statistical associations, making it difficult to source relevant training data for predictive modeling.
Innovation Solution
A Feature Graph data structure is employed, representing statistical associations between variables and topics, allowing for the construction of a graph database that connects nodes with edges indicating statistical measures, enabling users to search and access datasets based on statistical relevance rather than semantic matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional semantic matching approaches are used to discover data, then data about topics can be found, but data about statistically associated variables cannot be effectively discovered
Solution Approach 1:
The patent introduces a statistical association database as an intermediary component that stores pre-computed statistical associations between variables. This database acts as a mediator between the data discovery system and the underlying data sources, enabling efficient retrieval of statistically associated variables without requiring complex real-time analysis. The intermediary database resolves the contradiction by providing direct access to statistical relationships that semantic matching cannot capture.
Solution Approach 2:
The system changes the fundamental parameter of data organization from semantic relationships to statistical associations. By organizing data based on statistical measures (correlations, causal relationships, predictive relationships) rather than semantic meanings, the system enables discovery of variables that are statistically associated with target outcomes. This parameter change allows the system to retrieve data based on statistical relevance rather than semantic similarity.
2Reliability
If comprehensive data about all variables is collected for reliable decision-making, then prediction accuracy improves, but data access complexity increases
Solution Approach 1:
The patent extracts and isolates only the statistically associated variables needed for reliable predictions, separating them from the complete set of all available variables. By using statistical association queries to identify and extract relevant variables based on their relationships with target outcomes, the system reduces data access complexity while maintaining comprehensive coverage of important factors. This extraction principle allows reliable decision-making with a focused subset of variables rather than all possible variables.
3Loss of information
If semantic search is used to find data, then topic-relevant data is discovered, but variables that cause or predict the topic are missed
Solution Approach 1:
The patent creates a universal data discovery system that performs multiple functions: it can discover topic-relevant data through semantic matching, discover causal variables through causal relationship queries, and discover predictive variables through statistical association queries. This multi-functional system resolves the contradiction by enabling a single data discovery mechanism to access all types of relevant variables (semantic, causal, and predictive) rather than requiring separate systems for each type.
Data Source
AI summary
A system and associated methods for organizing, representing, finding, discovering, and accessing data. Embodiments represent information and data in the form of a data structure termed a “Feature Graph”. A Feature Graph 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, that is, a measurable object, characteristic or factor. An edge in a Feature Graph may represent a measure of a statistical association between a node and one or more other nodes that has been retrieved from a source or sources. Datasets that demonstrate or support the statistical association, or measure the associated variable are “linked to” from the Feature Graph.