Conditional Modifiers for Logical Field Mapping
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Solution Overview
Problem
Existing data abstraction models lack flexibility in allowing logical fields to reference multiple physical fields, necessitating separate queries and query result merging for accessing different data perspectives, such as current and historical data.
Innovation Solution
Implementing a data processing system that uses conditional modifiers with abstract queries, enabling logical fields to be mapped to multiple physical data structures and allowing users to specify which physical fields or combinations of fields a logical field should reference, with the option to create alternative data structures and populate them as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If logical fields are mapped to a single physical field in traditional abstraction models, then the mapping is simple and straightforward, but the system lacks flexibility to access multiple data perspectives (current and historical) through a single logical field
Solution Approach 1:
The logical field is enhanced to support multiple mappings to different physical fields (current and historical data). The same logical field name can map to different physical fields based on conditional modifiers, allowing a single logical field to serve multiple data access functions without requiring separate logical fields for each data perspective.
Solution Approach 2:
The mapping relationship between logical fields and physical fields is made dynamic rather than static. Conditional modifiers allow the mapping to change based on the query context, enabling the system to adapt the mapping behavior to different data access needs (current vs. historical) without restructuring the abstraction model.
2Loss of information
If separate queries are used to access current and historical data, then data from different perspectives can be retrieved, but the query process becomes complex and requires merging results manually
Solution Approach 1:
The system merges the functionality of multiple separate queries into a single query operation. By allowing conditional modifiers on logical fields, the system can retrieve both current and historical data in one query execution, automatically combining the results without requiring manual merging by the user.
Solution Approach 2:
The abstraction model with conditional modifiers acts as an intermediary layer between the user and the physical database structure. Users interact with the simplified logical field interface while the system handles the complexity of querying multiple physical fields, translating the high-level query into appropriate physical queries and merging results automatically.
3Reliability
If multiple logical fields are created to reference different physical fields, then each data perspective can be accessed separately, but the number of logical fields and queries increases
Solution Approach 1:
Instead of creating separate logical fields for different data perspectives, the system makes a single logical field universal by allowing it to map to multiple physical fields through conditional modifiers. This reduces the number of logical fields needed while maintaining accurate access to different data perspectives through context-dependent mapping.
Data Source
AI summary
Methods, systems, and articles of manufacture for enhancing abstract queries by utilizing conditional modifiers with logical fields are provided. Conditional modifiers allow for the referencing of multiple physical fields via a single logical field. Further, conditional modifiers facilitate the ability to specify, on an instance by instance basis, which physical field or combination of physical fields a particular logical field should reference.