Attribute Change Coalescing for Dataset Component Moves
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Solution Overview
Problem
Customizations involving multiple moves of a component in a dataset, interspersed with attribute changes, lead to inefficient recording and restoration of attribute changes due to changes in the composite ID, resulting in performance hits when re-applying these changes.
Innovation Solution
A method to coalesce changes by classifying them as single-component, cross-component, or cross-component changes affecting identity, and storing them accordingly, allowing for efficient application by saving single component changes under original scoped identifiers and cross-component changes in a dataset-wide list, with mappings for identity changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple attribute changes are recorded and restored for each component move, then the component state can be accurately tracked, but the performance deteriorates due to re-applying the same attribute changes repeatedly
Solution Approach 1:
The patent merges multiple attribute changes for the same component into a single coalesced change record. Instead of storing separate change records for each attribute modification, the system combines them and tracks only the net effect, eliminating redundant operations while maintaining accurate component state tracking.
Solution Approach 2:
The system changes the parameter representation from individual attribute change records to a coalesced change summary. By transforming multiple granular change parameters into a single aggregated parameter set, the system reduces the volume of data processed and applied, thereby improving performance while preserving state accuracy.
2Productivity
If attribute changes are saved under original scoped identifiers, then single-component changes can be efficiently applied, but cross-component changes require additional mapping overhead
Solution Approach 1:
The patent segments change management into two distinct pathways: one for single-component changes (saved under original scoped identifiers) and another for cross-component changes (requiring mapping). This segmentation allows the system to optimize for the common case of single-component changes while providing a structured approach for handling cross-component scenarios.
Solution Approach 2:
The system introduces a mapping mechanism as an intermediary layer between cross-component changes and the actual component identifiers. This mediator translates scoped identifiers to original identifiers, enabling cross-component changes to be handled systematically without disrupting the efficient single-component change pathway.
3Reliability
If all attribute changes are applied to every component, then no changes are lost, but redundant operations increase processing time
Solution Approach 1:
The system discards redundant attribute change records during the coalescing process, keeping only the necessary net changes. By identifying and discarding duplicate changes that would have no effect, the system recovers processing time while ensuring that all meaningful changes are preserved and applied correctly.
Data Source
AI summary
Embodiments of the invention provide for applying multiple attribute changes to components of a dataset. According to one embodiment, coalescing changes can comprise reading a definition of the dataset. For example, the definition can comprise an identity and a context for each of the plurality of components. A component tree can be generated representing the data set and based on the context and identity. An indication of one or more changes to the components of the data set can be received and the changes can be classified based on a type of each of the changes. For example, the type of the changes can comprise one or more of a single component change, a cross-component change, and a cross-component change the affects the identity of at least one of the components The changes can be coalesced based on the type of the changes.