ERP Error Context Analysis for ML Knowledge Resource Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The sheer volume and variety of knowledge resources present significant challenges in identifying and recommending the most relevant and beneficial resources for software systems, as customers face difficulty in navigating information overload and determining context-specific solutions to errors.
Innovation Solution
A machine learning-based system that analyzes error contexts and recommends tailored knowledge resources using a trained model to resolve errors in enterprise resource planning applications, leveraging data processing and machine learning techniques to automate the recommendation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual search and navigation of knowledge resources is used, then customers can find solutions to errors, but the process is time-consuming and inefficient due to information overload
Solution Approach 1:
The system enables self-service by automatically analyzing error messages and context, then recommending relevant knowledge resources without requiring manual search or navigation by customers
Solution Approach 2:
Manual search and navigation processes are replaced with automated machine learning-based analysis and recommendation systems that process error contexts and retrieve relevant knowledge resources programmatically
2Adaptability or versatility
If all knowledge resources are made available to customers, then comprehensive coverage is provided, but navigation and identification of relevant resources becomes increasingly difficult
Solution Approach 1:
The system extracts and presents only the most relevant knowledge resources from the comprehensive repository based on error context analysis, filtering out unrelated resources to simplify customer navigation
Solution Approach 2:
The system changes the parameter of resource presentation from displaying all available resources to displaying a filtered subset ranked by relevance, transforming the information presentation based on error context parameters
3Measurement precision
If comprehensive error analysis is performed to ensure accurate recommendations, then recommendation quality improves, but system complexity and computational requirements increase
Solution Approach 1:
The error analysis process is segmented into distinct components: error message parsing, context extraction, resource matching, and recommendation ranking, allowing each component to be optimized independently while maintaining overall accuracy
Data Source
AI summary
Disclosed herein are a system, method, and computer program product embodiments for recommending knowledge resources to resolve errors at a source and/or target system. For example, a request to post data to a target system is received from a source system. A determination is made that an error occurred with respect to the request. An error context for the error is determined. The error context comprises information describing the error and information describing the system. A representation of the error context is provided as an input to an ML model. The ML model is configured to predict a knowledge resource for resolving the error based on the error context. A prediction indicating the knowledge resource for resolving the error is received from the ML model. A recommendation to apply the knowledge resource on the source and/or target system is provided via a user interface based on the prediction.