Analytics Engine for Computer Infrastructure Error Diagnosis
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Solution Overview
Problem
Modern computer infrastructures are complex and time-consuming to analyze due to their extreme complexity, making it difficult to identify the initial cause of application errors without frequent reprogramming and manual data mining, which can lead to ineffective data distribution and erroneous investment decisions in financial institutions.
Innovation Solution
An apparatus comprising a plurality of devices generating messages, a data store, and an analytics engine that correlates these messages to produce a systems model, allowing for rapid identification of application errors and malfunctions without periodic reprogramming, by analyzing historic and real-time data to determine causality sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data mining and periodic reprogramming are used to analyze computer infrastructure, then the system can identify errors and malfunctions, but the analysis process becomes time-consuming and requires frequent human intervention
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing messages and data items in the data store before analysis is needed. The analytics engine pre-processes data and maintains an updated systems model, so when an error occurs, the analysis can be performed rapidly without manual intervention or time-consuming data collection.
Solution Approach 2:
The analytics engine automatically analyzes messages, correlates data items, and updates the systems model without requiring manual reprogramming or human intervention. The system serves itself by autonomously identifying errors and malfunctions, eliminating the need for periodic manual analysis and reducing time loss.
2Adaptability or versatility
If the analysing system is reprogrammed regularly to accommodate changes in computer infrastructure, then the system can adapt to new devices and users, but the reprogramming process is time-consuming and error-prone
Solution Approach 1:
The systems model is dynamic and automatically updates to reflect changes in the computer infrastructure. As new devices, users, or configurations are added, the analytics engine continuously collects new messages and data items, automatically updating the systems model without requiring manual reprogramming. This dynamic adaptation eliminates maintenance complexity while preserving adaptability.
Solution Approach 2:
The system uses feedback from continuously collected messages and data items to automatically adjust and update the systems model. When infrastructure changes occur, the analytics engine receives feedback in the form of new messages, correlates new data items, and automatically updates the model to reflect the current state, eliminating the need for manual reprogramming.
3Measurement precision
If detailed knowledge of the computer infrastructure structure is required for analysis, then the system can provide accurate diagnosis, but the system becomes difficult to maintain and update
Solution Approach 1:
The analytics engine automatically builds and maintains the systems model by correlating messages and data items from the infrastructure itself. The system extracts and learns the infrastructure structure autonomously without requiring manual configuration or detailed prior knowledge, thereby maintaining diagnosis accuracy while reducing system complexity and ease of maintenance.
Solution Approach 2:
The patent replaces manual mechanical processes of infrastructure mapping and system configuration with an automated information processing system. The analytics engine uses message correlation and data item analysis to automatically discover and model the infrastructure structure, substituting automated computational processes for manual system setup and maintenance.
Data Source
AI summary
A system and method for analyzing a computer infrastructure. The system and method analyses messages sent and received within the computer infrastructure to determine a system model and exception data.