Data Segmentation Manager Using Distinction Entities
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Solution Overview
Problem
Conventional manual data segmentation techniques are time-consuming, complex, and difficult to coordinate, especially when attempting to manage large datasets based on user-defined criteria, as they require manual implementation and lack efficient mechanisms for filtering and reducing data segments.
Innovation Solution
A computer system with a segmentation manager that generates a reduced segment of a population set for a course of activities using one or more distinction entities, including a set handler to determine the population set and an entity handler to select relevant members based on predetermined criteria, thereby optimizing data segmentation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual data segmentation techniques are used, then data can be segmented based on user-defined criteria, but the process becomes time-consuming and complex
Solution Approach 1:
The system pre-generates multiple distinct segments from the population set in advance, storing them for later use. This preliminary segmentation action eliminates the need for time-consuming manual filtering when actual segmentation is needed, as the segments are already prepared and can be directly applied to courses of activities.
Solution Approach 2:
The system automatically performs segmentation operations using predetermined criteria and distinction entities without requiring manual intervention. The segmentation engine autonomously generates reduced segments by applying filtering logic based on distinction entities, making the system self-sufficient and eliminating the time loss associated with manual data segmentation.
2Measurement precision
If manual data segmentation is performed, then data can be filtered based on developer knowledge, but the process becomes highly complex and difficult to coordinate
Solution Approach 1:
The system divides the population set into multiple distinct segments based on different distinction entities, with each segment representing a specific subset of the population. This segmentation approach maintains precision by creating well-defined groups while reducing complexity, as each segment is independently generated and can be managed separately through automated processes.
Solution Approach 2:
The system uses distinction entities as parameters to automatically filter and segment the population set. By changing the distinction entity parameters (such as demographic characteristics, behavioral attributes, or other defining features), the system generates different segments without requiring complex manual coordination, thereby maintaining segmentation accuracy while simplifying the overall process.
3Ease of manufacture
If conventional manual segmentation techniques are used, then data can be organized, but productivity is significantly reduced
Solution Approach 1:
The segmentation engine automatically organizes the population set into distinct segments based on predetermined criteria and distinction entities without requiring manual data organization efforts. This self-service capability maintains the ease of data organization while dramatically improving productivity, as the system can process and segment large datasets autonomously and efficiently.
Solution Approach 2:
The system pre-organizes the population set into multiple distinct segments in advance, storing them for future use. This preliminary organization action eliminates the need for repeated manual data organization when segmentation is required, thereby maintaining data organization capability while significantly increasing productivity by making segmented data immediately available for courses of activities.
Data Source
AI summary
In accordance with aspects of the disclosure, systems and methods are provided for generating a reduced segment of a population set for a course of activities based on one or more distinction entities. The systems and methods may be configured to determine the population set for the course of activities. The population set may include a first number of members identified by a first distinction entity. The systems and methods may be configured to select a second distinction entity related to the first number of members of the population set. The systems and methods may be configured to generate the reduced segment of the population set for the course of activities by selecting a second number of members from the first number of members identified by the second distinction entity based on predetermined criteria.