Enterprise Configuration Data Collection and Categorization
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Solution Overview
Problem
The deployment and configuration of enterprise systems are labor-intensive and complex, often requiring specialized consultants to set up and manage, due to the need for precise configuration and tracking of various business processes and data analytics.
Innovation Solution
A configuration data collection and categorization system with a data crawler module, scheduler, categorization, and ranking modules, which collects, classifies, and recommends configuration data changes based on metadata, allowing for automated setup and optimization of enterprise systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If enterprise systems are manually configured and set up by specialized consultants, then the system configuration precision and business process accuracy are improved, but the labor intensity and complexity increase significantly
Solution Approach 1:
The system enables self-service configuration by automatically collecting configuration data from multiple sources, categorizing it using machine learning models, and generating recommended configurations without requiring specialized consultants. The automated data collection module crawls configuration data from various systems, and the categorization module processes this data to produce ready-to-use configuration recommendations.
Solution Approach 2:
The patent replaces the manual mechanical process of consultant-driven configuration with an automated computational system. Machine learning models and automated data processing algorithms substitute for human expert analysis, automatically categorizing configuration data and generating recommendations, thereby eliminating the need for manual consultant intervention while maintaining configuration precision.
2Measurement precision
If specialized consultants are hired to design and set up enterprise systems, then the configuration accuracy and business process tracking precision are improved, but the time consumption and cost increase
Solution Approach 1:
The system performs preliminary action by pre-collecting and pre-categorizing configuration data from multiple sources before the actual enterprise system deployment. The automated data collection module continuously gathers configuration data, and the machine learning models pre-process this data into categorized configurations that are ready for immediate application, eliminating the need for time-consuming on-site consultant analysis during deployment.
Solution Approach 2:
The patent replaces time-consuming manual consultant analysis with automated machine learning-based data processing. The system automatically categorizes configuration data and generates recommendations in minutes, whereas manual consultant analysis would take days or weeks, thereby dramatically reducing the time required while maintaining or improving configuration accuracy.
3Adaptability or versatility
If manual configuration and setup processes are used, then the system can be customized to precisely describe organizational workflows, but the labor intensity and resource requirements increase
Solution Approach 1:
The system enables self-service customization by automatically analyzing organizational data from multiple sources and generating tailored configuration recommendations specific to each organization's workflows and requirements. The machine learning models learn from the collected configuration data to produce customized configurations without requiring manual consultant intervention, thereby maintaining high adaptability while improving productivity.
Solution Approach 2:
The patent introduces an intermediary automated data processing layer between raw organizational data and final system configuration. The machine learning-based categorization module acts as a mediator that transforms unstructured organizational workflow descriptions into structured configuration parameters, enabling precise business process customization through automated means rather than manual configuration.
Data Source
AI summary
In an embodiment, a method is provided for collecting configuration data. In this example, configuration data associated with an application is searched. Additionally, metadata associated with the configuration data is searched. Changes made to the configuration data are detected, and the changes and associated metadata are stored in a storage device. The changes are then categorized based on the metadata.