Graph Database Matching Boolean Expressions
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Solution Overview
Problem
Existing market research methods struggle to efficiently match return panelists or newly enrolled panelists to projects with low acceptance rates and to reroute panelists who fail to qualify for one survey to another, leading to suboptimal utilization of resources.
Innovation Solution
A graph database is used to match entities by evaluating Boolean expressions, allowing for rapid identification of relevant facts and criteria, thereby efficiently matching panelists to market research projects with constraints on quota cells and demographic requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional matching methods are used to assign panelists to projects, then panelists can be assigned to projects, but the utilization efficiency of panelists is low and acceptance rates are suboptimal
Solution Approach 1:
The system transforms the matching approach by changing from simple attribute-value matching to Boolean expression evaluation. Panelist profiles and project requirements are represented as sets of Boolean expressions, enabling complex logical conditions (AND, OR, NOT) to be evaluated systematically. This parameter transformation allows the system to handle sophisticated matching criteria while maintaining computational efficiency through the graph database structure.
Solution Approach 2:
The patent introduces an intermediary layer between panelists and projects: the graph database with vertices representing panelists, projects, and their relationships through Boolean expressions. This intermediary structure enables efficient traversal and evaluation of complex matching criteria without requiring direct complex queries between panelist attributes and project requirements, thus improving matching efficiency while managing system complexity.
2Productivity
If panelists are matched to projects with high acceptance rates, then acceptance rates improve, but opportunities to utilize panelists in low acceptance rate projects are lost
Solution Approach 1:
The system implements dynamic matching where the evaluation of Boolean expressions and the selection of projects are not static but adapt based on current panelist profiles, project requirements, and availability. The graph database enables real-time traversal and evaluation, allowing the system to dynamically adjust matches based on changing conditions, thus improving both acceptance rates and the ability to assign panelists to diverse project types including low acceptance rate projects.
Solution Approach 2:
The Boolean expression evaluation framework serves multiple functions: it can evaluate simple attribute matches, complex logical conditions, inclusion/exclusion criteria, and demographic requirements all through the same mechanism. This universal approach allows the system to handle diverse matching scenarios uniformly, improving adaptability while maintaining consistent acceptance rate optimization across different project types.
3Measurement precision
If complex Boolean expressions are evaluated to match panelists to projects, then matching precision improves, but computational time increases
Solution Approach 1:
The system performs preliminary actions by pre-structuring the data in a graph database where panelist attributes, project requirements, and their relationships are organized as vertices and edges before matching occurs. This pre-organization allows the evaluation of Boolean expressions to proceed efficiently during the actual matching process, as the computational work of traversing relationships and evaluating conditions is streamlined by the pre-established graph structure, thus reducing computational time while maintaining high matching precision.
Solution Approach 2:
The patent replaces traditional mechanical query evaluation methods with a graph database traversal approach. Instead of evaluating complex Boolean expressions through conventional database queries that require scanning and filtering large datasets, the system uses graph traversal to navigate pre-established relationships, substituting the mechanical query evaluation process with a more efficient graph-based evaluation mechanism that reduces computational time while preserving matching precision.
Data Source
AI summary
A method of matching a first entity to a second entity by evaluating Boolean expressions includes identifying a set of criteria vertices for a second entity vertex by traversing a graph database in a manner constrained to fact vertices identified for the second entity. The graph database relates fact vertices to the criteria vertices by edges corresponding to Boolean expressions for satisfying criteria for matching first entities to second entities. The method additionally includes selecting one of the first entities based on the criteria vertices of the set. The method further includes matching the first entity to the second entity based on the selection.