ML Semantic Analysis for Enterprise Database Correlation
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Solution Overview
Problem
Large enterprises face challenges in integrating and streamlining operations due to the proliferation of siloed custom software applications, which hinders their ability to grow, innovate, and meet regulatory requirements, and existing cloud-based remote network management platforms struggle to efficiently analyze and correlate information across databases.
Innovation Solution
The implementation of a machine learning-based framework for semantic analysis of textual information in incident, online chat, knowledgebase, and skills databases, utilizing techniques like clustering, term frequency, word embedding, and vector representations to provide insights and recommendations for improving database management and user support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tools are used to analyze incident databases, then the system structure remains simple, but the system cannot find correlations and interactions in information within large databases
Solution Approach 1:
The patent introduces an intermediary system comprising computational instances with machine learning capabilities that act as a mediator between the enterprise's incident databases and the analysis requirements. This intermediary platform processes and correlates information across multiple databases (incident, online chat, knowledgebase, skills) using ML techniques, thereby enabling correlation discovery without requiring the enterprise's own systems to become complex.
2Quantity of substance
If the enterprise grows and databases increase in size, then more information is available for analysis, but conventional tools become incapable of finding correlations
Solution Approach 1:
The patent applies parameter changes by transforming the analysis approach from conventional rule-based methods to machine learning-based semantic analysis. The system uses ML models to process textual information, perform clustering, generate embeddings, and identify patterns in large databases, thereby maintaining analysis productivity despite increasing database sizes that render conventional tools ineffective.
3Loss of information
If multiple databases are analyzed simultaneously, then more comprehensive insights are obtained, but the complexity of integrating and correlating information increases
Solution Approach 1:
The patent implements a universal analysis platform that handles multiple database types (incident, online chat, knowledgebase, skills) through a unified machine learning framework. The system performs multiple functions including text preprocessing, clustering, embedding generation, and correlation analysis across all databases simultaneously, thereby obtaining comprehensive insights without proportionally increasing integration complexity.
Data Source
AI summary
The embodiments herein provide a framework for and specific implementations of machine learning (ML) analysis of incident, online chat, knowledgebase, skills, and perhaps other types of databases. The ML techniques described herein may include various forms of semantic analysis of textual information in these databases, such as clustering, term frequency, word embedding, paragraph embedding, and potentially other techniques. Advantageously, use of ML in the specific ways described herein can provide insights into this textual information that otherwise would be impossible to determine in an accurate or concise fashion.