Relationship Model Automates IT Data Correlation
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Solution Overview
Problem
Managing unstructured data in IT environments is challenging due to its distributed and dynamic nature, making it costly and time-consuming to extract valuable information, and posing difficulties for IT managers in ensuring data accessibility, storage, and compliance with auditing requirements.
Innovation Solution
A relationship model system automatically recognizes and correlates data from multiple sources, storing relationships remotely and allowing for queries and actions based on these correlations, enabling efficient management and extraction of insights from unstructured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual human analysis is used to extract insights from unstructured data, then the cost and time required increase significantly, but the ability to derive valuable insights from distributed and dynamic data remains limited
Solution Approach 1:
The system enables self-service by allowing users to query the relationship model without requiring manual analysis of unstructured data. The automated relationship recognition and correlation mechanisms perform the analysis work that would otherwise require human intervention, making the system serve itself in extracting insights from distributed data.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computer-based relationship recognition systems. The relationship model uses automated algorithms to correlate data from multiple sources, substituting the manual human analysis mechanism with an automated information processing system that can handle distributed and dynamic data efficiently.
2Ease of operation
If data are stored in distributed locations to improve accessibility, then the ease of retrieving specific data increases, but the complexity of managing and correlating data from multiple sources increases
Solution Approach 1:
The relationship model acts as an intermediary layer between users and the distributed data sources. Instead of users directly managing complex distributed data, the relationship model mediates by providing a unified interface that automatically correlates data from multiple sources, simplifying the user interaction while maintaining the benefits of distributed storage.
Solution Approach 2:
The relationship model provides universal access to data from multiple diverse sources through a single unified interface. It performs multiple functions including data correlation, relationship recognition, and query processing, making the system universally applicable to various data types and sources without increasing user complexity.
3Productivity
If automated relationship recognition is implemented to reduce manual effort, then the productivity increases, but the system complexity and initial setup requirements increase
Solution Approach 1:
The system performs preliminary action by pre-establishing relationship models and correlation rules before actual data processing occurs. The relationship model is built in advance with predefined correlations between data sources, allowing automated processing to proceed efficiently without requiring complex real-time decision-making during data analysis operations.
Data Source
AI summary
A relationship model system automatically recognizes relationships among data in a local IT environment. Such data may be derived from multiple sources, such as multiple devices and/or software applications of different types. Such data are collected automatically, remotely, and transparently from the local IT environment. Relationships among the data are recognized automatically by correlating data from the multiple sources. Records of such relationships are stored remotely in a relationship model. The system may draw conclusions based on the recognized relationships and take actions in response to those conclusions.