Contextual Network Access Optimizer for Enterprise Data
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Solution Overview
Problem
Conventional search engines are inaccurate in searching enterprise data due to their failure to consider the semantic meaning of keywords, leading to incorrect results when searching for business objects and documents.
Innovation Solution
A contextual network system that uses a meta-model semantic network manager to register and configure business applications, providing optimized access to contextual network data by calculating distances and energy between nodes in a contextual network graph, and employing a self-learning algorithm to optimize configuration settings for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines search for keywords in enterprise data, then search coverage is improved, but search accuracy deteriorates
Solution Approach 1:
The patent introduces a semantic network as an intermediary layer between the search engine and enterprise data. This semantic network contains business objects, concepts, and relationships that mediate the search process, enabling the system to understand semantic meaning rather than just matching keywords. The semantic network acts as a bridge that translates keyword searches into semantically meaningful results.
Solution Approach 2:
The patent adds a semantic dimension to the traditional keyword-based search. Instead of operating solely in the keyword-matching dimension, the system incorporates a semantic dimension through the semantic network, which includes business objects, concepts, and relationships. This dimensional expansion allows the search to capture semantic meaning while maintaining keyword search capabilities.
2Productivity
If contextual network data is optimized for access, then system performance is improved, but configuration complexity increases
Solution Approach 1:
The patent implements a self-learning algorithm that automatically optimizes configuration settings for contextual network access. The system monitors access patterns, analyzes performance data, and autonomously adjusts configuration parameters without requiring manual intervention. This self-service approach eliminates the need for complex manual configuration while maintaining optimal performance.
Solution Approach 2:
The system incorporates feedback mechanisms where access patterns and performance metrics are continuously monitored and fed back into the optimization process. The self-learning algorithm uses this feedback to iteratively improve configuration settings, adapting to changing access patterns and optimizing performance automatically over time.
Data Source
AI summary
A method and apparatus for optimizing access to a contextual network are described. The apparatus has a registration manager module, a configuration manager module, an access manager module, and an access optimizer module. The registration manager module registers business applications operating in the contextual network of a server with registration data having a set of parameters to define the business applications. The configuration manager module determines initial configuration settings for the business applications to identify parts of the contextual network relevant to the business applications. The access manager module provides the business applications with access to contextual network data based on distances between nodes in a contextual network graph of the contextual network data. The access optimizer module collects measurements of the access of the business applications to the contextual network data, and computes future optimal configuration settings for the business applications.