Flexible Data Population Selection Framework
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Solution Overview
Problem
Business applications lack flexibility in selecting data populations for processing and updating, with predefined criteria limiting user control and often requiring costly modifications or risking data integrity issues.
Innovation Solution
A flexible selection and update framework using metadata to define selection and update tools, allowing users to customize data processing and prevention of unintended data modifications through a user-friendly interface and consistent communication protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined selection criteria are used in business applications, then data processing can be performed with established rules, but user flexibility and control over data selection are limited
Solution Approach 1:
The selection framework is segmented into distinct components: selection criteria definitions, population selection engine, and update mechanisms. This allows users to configure specific selection criteria independently while maintaining a structured overall framework, resolving the contradiction between flexibility and complexity.
Solution Approach 2:
The system transitions from static predefined criteria to dynamic user-configurable selection criteria. Users can modify selection parameters and criteria at runtime, enabling the system to adapt to changing requirements without requiring complete redesign, thus improving flexibility while managing complexity through standardized interfaces.
2Adaptability or versatility
If predefined update processes are used in business applications, then data updates can be performed with established procedures, but user control over update operations is limited and modifications require costly engineering resources
Solution Approach 1:
The update framework enables users to independently configure and execute data update operations without requiring engineering resources. Users can define update criteria, select target populations, and control update execution through the framework's interface, making the system self-sufficient for common update needs and eliminating costly modification requirements.
3Ease of operation
If direct access to data objects is allowed for modification, then users can update data freely, but data integrity may be compromised with unintended modifications
Solution Approach 1:
The update framework acts as an intermediary layer between users and data objects. It provides controlled access through selection criteria and update rules, allowing users to easily modify data while maintaining data integrity through standardized validation and execution mechanisms. This mediator approach resolves the contradiction by enabling ease of operation without compromising reliability.
Solution Approach 2:
The framework incorporates feedback mechanisms that validate update operations before execution and provide confirmation of completed updates. This ensures data integrity by preventing unintended modifications while maintaining ease of operation through user-friendly interfaces and clear operation status reporting.
4Adaptability or versatility
If extensive modifications are made to business application processes, then custom data selection and update requirements can be met, but maintenance costs increase and system stability may be affected
Solution Approach 1:
The selection and update frameworks provide universal, multi-functional capabilities that handle diverse data selection and update requirements through standardized mechanisms. Rather than requiring custom modifications for each specific need, the frameworks offer configurable parameters and criteria that can accommodate various business requirements, reducing maintenance complexity while preserving customization capability.
Data Source
AI summary
Tools providing a flexible selection framework for automated processes. The framework can allow end-users to define their own selection criteria to select a data population to be processed (for example, by a business application). Hence, the tools provide enhanced control over what data is provided to which process. Some such tools employ metadata to define what information the client process needs, how the results will be returned to the client process, and/or what selection tools should be available to select data for the client process, as well as the available data selection tools, which can include both tools provided with a business application as well as third-party and/or user supplied selection tools. The framework might also provide an application programming interface that ensures consistent communication between the population selection engine and the selection tools themselves.