Dynamic Consolidated Data Update via Granular Recalculation
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Solution Overview
Problem
Existing database systems face challenges in efficiently updating and integrating granular data with consolidated data, often resulting in delayed reflections of changes or the need for recalculating all high-level data, which can lead to data integrity and availability issues.
Innovation Solution
The method involves processing granular data on demand to produce updated data that can be integrated with consolidated data, allowing for comparison and augmentation, while preserving data integrity by calculating only necessary values without recalculating all high-level data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all consolidated data is recalculated whenever base data changes, then data integrity is maintained, but processing time and computational resources are excessively consumed
Solution Approach 1:
The patent segments the data update process by identifying and processing only the specific consolidated data items that are directly affected by base data changes, rather than recalculating all consolidated data. This is achieved through tracking dependencies between base data and consolidated data, allowing selective updates that maintain integrity while reducing processing time.
Solution Approach 2:
The system performs partial recalculation by calculating only the necessary consolidated data values that need updating, rather than performing a complete recalculation of all consolidated data. This partial action approach maintains data integrity for affected items while avoiding the excessive computational overhead of full recalculation.
2Loss of information
If consolidated data is updated frequently to reflect base data changes, then data availability is improved, but system performance and resource usage deteriorate
Solution Approach 1:
The system implements periodic or event-driven updates of consolidated data based on when base data changes occur, rather than continuous or frequent updates. This approach ensures data availability when needed while allowing the system to maintain better performance between update events by not constantly processing data.
Solution Approach 2:
The system prepares and identifies which consolidated data items need updating in advance by tracking data dependencies, so that when base data changes, only the pre-identified affected consolidated data is updated. This preliminary identification reduces the actual update workload and improves system performance.
3Adaptability or versatility
If granular data is processed to produce consolidated data, then data analysis capability is enhanced, but the complexity of data management increases
Solution Approach 1:
The patent introduces an intermediary layer that manages the processing and transformation of granular data into consolidated data. This intermediary mechanism handles the complexity of data processing, grouping, and aggregation, while presenting a simplified interface to users who can easily access and analyze consolidated data without needing to understand the underlying processing complexity.
Data Source
AI summary
Techniques and solutions are provided for updating or augmenting consolidated data that is produced using base data. The consolidated data can include data that is aggregated by various grouping criteria. After a set of consolidated data is determined, the base data may change, one or more rules used to calculate the consolidated data may change, or it may be desired to see data that is more granular than that included in the consolidated data. After consolidated data is provided to a user, a user issues a data augmentation request. The data augmentation request causes the base data, which may have been updated, to be processed to provide updated data, where the processing includes grouping operations used in producing the consolidated data. The updated data is provided to a client in response to the data augmentation request.