Segmented Risk Analysis Model for Infrastructure Change Prediction
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Solution Overview
Problem
Conventional risk assessment for technology infrastructure changes focuses on data related to the change itself rather than historical relationships between physical devices, applications, and individuals involved, leading to inadequate prediction of failure risks.
Innovation Solution
A predictive modeling apparatus and method that uses machine-readable memory to store data objects containing infrastructure change identifiers, device identifiers, and application identifiers, generating a failure index based on historical information to predict the probability of infrastructure change failure by analyzing relationships between change elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional risk assessment focuses on data surrounding the infrastructure change itself, then the assessment process is simple and straightforward, but the prediction accuracy of failure risk is insufficient
Solution Approach 1:
The risk assessment system is segmented into multiple independent components: change data collector, historical data collector, relationship graph generator, and risk predictor. Each component handles specific data types (change metadata, device information, application information, historical outcomes) and processes them separately before integration, improving prediction accuracy while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system transitions from traditional single-dimensional risk assessment (focusing only on change data) to multi-dimensional assessment by incorporating historical relationships between devices, applications, and changes. The relationship graph adds spatial and temporal dimensions to the data structure, enabling more accurate failure risk prediction through holistic analysis of interconnected infrastructure elements
2Reliability
If the system collects and analyzes historical relationships between devices, applications and changes, then the prediction capability improves, but the data processing complexity and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing historical infrastructure change data, device data, and application data in structured formats before actual risk assessment is needed. The relationship graph between infrastructure elements is pre-computed and stored, enabling rapid retrieval and analysis during real-time risk prediction without requiring extensive processing time when failures need to be predicted
Solution Approach 2:
The system creates simplified copies of complex infrastructure relationships through the relationship graph structure. Instead of processing raw, unstructured historical data during risk assessment, the system uses pre-processed copied representations of device-changes, application-changes, and device-application relationships, significantly reducing computational time while maintaining prediction reliability
Data Source
AI summary
Apparatus and methods for reducing infrastructure failure rates. The apparatus and methods may compile and store data related to the organizational segments associated with the approval and implementation of an infrastructure change. Variables may be derived from the data using a range of methods, and multiple variable values may be consolidated. A model may be developed based on the values and relationships of the derived variables. The model may be applied to assess the risk involved in a prospective infrastructure change.