Application Discovery Classifier for Cross-Entity Process Identification
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Solution Overview
Problem
Identifying true applications behind processes is challenging due to insufficiently descriptive process names and the cumbersome, inaccurate nature of current methods requiring numerous rules, especially in data center computing environments.
Innovation Solution
A method for discovering software applications using metadata analysis and artificial intelligence (AI) and machine learning (ML) to identify correlations among reports from multiple entities, generating a software application classifier that automatically identifies processes associated with applications across entities, and providing a graphical user interface (GUI) for validation and configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rule-based methods are used to identify applications behind processes, then identification can be performed, but the process becomes cumbersome and inaccurate due to the large number of rules required
Solution Approach 1:
The patent replaces the mechanical rule-based system with an AI/ML-based automated classification system. The system uses machine learning models to analyze process metadata and automatically identify applications, eliminating the need for numerous manual rules while improving identification accuracy through pattern recognition and correlation analysis across multiple entities.
Solution Approach 2:
The system enables self-service by allowing the AI/ML model to automatically learn and adapt to identify applications without requiring manual rule configuration. The model autonomously analyzes process data, identifies patterns, and generates classifications, reducing human intervention and rule maintenance while improving accuracy over time.
2Productivity
If process names are used to identify applications, then identification can be performed, but the process names are often not sufficiently descriptive
Solution Approach 1:
The patent adds another dimension to the identification process by analyzing multiple attributes beyond just process names. The system examines process metadata, behavioral patterns, resource usage, and correlations across entities to create a multi-dimensional classification approach, thereby recovering lost descriptive information while maintaining high identification efficiency.
Solution Approach 2:
The AI/ML-based classifier serves multiple functions: it identifies applications, classifies processes, discovers patterns, and provides descriptive information generation. This universal approach handles various identification challenges simultaneously, improving productivity while preserving descriptive information through comprehensive analysis.
3Measurement precision
If manual identification methods are used, then application identification can be performed, but manual effort is required which reduces efficiency
Solution Approach 1:
The system implements self-service through automated AI/ML-based classification that performs application identification without manual intervention. The model autonomously processes process data, generates classifications, and updates its knowledge base, eliminating time-consuming manual efforts while maintaining or improving identification accuracy through continuous learning.
Solution Approach 2:
The system performs preliminary action by pre-training the AI/ML model with extensive process data and patterns before deployment. This preliminary training enables the system to quickly and accurately identify applications in production environments without requiring manual effort during operation, saving time while maintaining high accuracy.
4Adaptability or versatility
If current application discovery methods are used, then applications can be identified, but the methods are not scalable to data center computing environments with tens or hundreds of processes
Solution Approach 1:
The patent replaces manual and rule-based mechanical systems with an automated AI/ML system that scales efficiently. The machine learning model can process and analyze large volumes of process data from data center environments with tens or hundreds of processes per application, providing scalable identification capability while managing system complexity through automated pattern recognition and correlation analysis.
Solution Approach 2:
The system merges data from multiple entities and processes into a unified analysis framework. By combining process metadata, behavioral patterns, and correlation information across entities, the system achieves scalable identification that handles complex data center environments efficiently, reducing the effective complexity through integrated analysis.
Data Source
AI summary
Methods and systems for discovering software applications are disclosed. A plurality of reports corresponding to a plurality of candidate applications is obtained from a plurality of entities, wherein each report comprises information corresponding to a plurality of software processes associated with one of the plurality of candidate applications and running on one of the plurality of entities. Correlations among the plurality of reports corresponding to the plurality of candidate applications from the plurality of entities are identified. A software application classifier for automatically identifying one or more software processes associated with an application that is used across multiple entities is generated based at least in part on the identified correlations. The software application classifier is provided to at least one of the plurality of entities.