Impact Analysis for Data Transformation Systems
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Solution Overview
Problem
In complex data processing systems, determining the scope and impact of changes is challenging due to wide-ranging effects on directly and indirectly affected components, making it difficult to estimate costs and resource allocation for necessary changes.
Innovation Solution
A method that analyzes proposed changes by receiving information about logical datasets, transformations, and proposed changes, calculating metrics to measure direct and indirect impacts, and associating costs, allowing for the identification of affected components and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive impact analysis is performed to identify all affected components, then accuracy of change scope estimation is improved, but computational time and system complexity increase
Solution Approach 1:
The system pre-generates and stores impact analysis information before changes are implemented. By analyzing the data model, transformations, and relationships in advance, the system creates a knowledge base that enables rapid impact assessment when changes occur, avoiding the need to perform comprehensive analysis from scratch during actual change scenarios.
Solution Approach 2:
The impact analysis is divided into distinct components: direct impact analysis (immediate effects of changes) and indirect impact analysis (cascade effects through transformations and relationships). This segmentation allows the system to handle different types of impact separately and efficiently, reducing overall computational complexity while maintaining comprehensive coverage.
2Reliability
If detailed analysis of transformations and data relationships is performed, then completeness of impact identification is improved, but resource allocation complexity increases
Solution Approach 1:
The system uses stored impact analysis information as feedback to guide resource allocation decisions. By continuously referencing pre-analyzed impact data, the system can make informed resource allocation decisions without repeatedly performing complex analysis, reducing operational complexity while maintaining reliability.
Solution Approach 2:
The patent introduces an intermediary layer of stored impact analysis information that mediates between the complex data relationships and the resource allocation process. This intermediary contains preprocessed information about transformations, data flows, and relationships, allowing the system to query impact without directly traversing complex relationships during resource allocation.
3Adaptability or versatility
If manual analysis methods are used to determine change impact, then flexibility in handling complex scenarios is improved, but productivity and consistency decrease
Solution Approach 1:
The system performs impact analysis automatically without requiring manual intervention. By encoding the data model, transformations, and relationships in a structured format, the system enables automated impact assessment that is both fast and adaptable to different change scenarios, eliminating the trade-off between manual flexibility and automated speed.
Solution Approach 2:
The system represents the data processing system as a parameterized model with defined relationships and transformations. By changing parameters (such as data fields, transformations, or relationships) in the model, the system can automatically adapt to different scenarios and compute impact, providing both flexibility and high productivity through parameter-driven analysis.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for impact analysis. One of the methods includes receiving information about at least two logical datasets, the information identifying, for each logical dataset, a field in that logical dataset and format information about that field. The method includes receiving information about a transformation identifying a first logical dataset from which the transformation is to receive data and a second logical dataset to which the transformed data is provided. The method includes receiving one or more proposed changes to at least one of the fields. The method includes analyzing the proposed changes based on information about the transformation and information about the first logical dataset and the second logical dataset. The method includes calculating metrics of the proposed change based on the analysis. The method also includes storing information about the metrics.