Analytics Engine Controller for Dynamic Issue Abstraction
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Solution Overview
Problem
Existing analytics engines are limited in their ability to recognize and address issues beyond previously encountered specific problems, as they only suggest solutions based on identical previous incidents, lacking the capability to generalize or correlate related issues for broader insight and failure prediction.
Innovation Solution
The analytics engine controller stores and correlates problems to generate abstracted issues, allowing for the recognition of interrelated problems and sharing of insights across different levels, using service level agreements, contracts, and confidentiality agreements to determine sharing partners and levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the analytics engine only stores and matches identical previous issues, then the solution suggestion is simple and fast, but the system cannot recognize or address new or correlated problems beyond exact matches
Solution Approach 1:
The patent introduces an analytics engine controller as an intermediary component that sits between the data sources and the existing analytics engine. This controller performs the complex functions of storing issues in structured format, abstracting issues to identify correlations, and determining sharing partners, while the underlying analytics engine remains relatively simple. The intermediary handles the adaptability requirements without requiring the entire system to become complex.
Solution Approach 2:
The system is segmented into distinct functional components: issue storage, issue abstraction/correlation analysis, and sharing partner determination. Each component handles a specific aspect of the problem, allowing the system to achieve high adaptability through modular design while keeping individual component complexity manageable.
2Loss of information
If the system shares all issues with all partners, then information sharing is maximized, but confidentiality and service level agreement compliance cannot be ensured
Solution Approach 1:
The patent applies different sharing policies to different issues based on their characteristics and the relationships between entities. Rather than a uniform sharing approach, the system determines sharing partners individually for each issue, considering factors like confidentiality agreements and service level agreements. This localized decision-making ensures compliance while maximizing appropriate information sharing.
Solution Approach 2:
The system changes the parameters of information sharing dynamically based on the specific issue being analyzed. By evaluating confidentiality requirements, service level agreements, and entity relationships, the system adjusts who receives which information, transforming a static all-or-nothing sharing model into a dynamic, compliant sharing mechanism.
3Reliability
If the system abstracts and correlates all issues extensively, then failure prediction and insight generation improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by storing issues in a structured format with key attributes identified upfront, and by maintaining an abstraction layer that pre-processes issue data. This preliminary structuring and abstraction enable faster correlation analysis and failure prediction when needed, reducing the computational burden during actual analysis operations.
Data Source
AI summary
A system and method include an analytics engine of a system of an enterprise sharing issues encountered in the system. The method includes storing considerations to be applied to the sharing. The considerations include a service level agreement, contract, or confidentiality agreement. The method also includes deciding to share one or more of the issues based on applying the considerations to each of the issues, and sharing the one of more of the issues with one or more sharing partners based on the applying the considerations.