18121252. TOPOLOGY EXPLORER FOR MESSAGE-ORIENTED MIDDLEWARE USING MACHINE LEARNING TECHNIQUES simplified abstract (Dell Products L.P.)

TOPOLOGY EXPLORER FOR MESSAGE-ORIENTED MIDDLEWARE USING MACHINE LEARNING TECHNIQUES

Organization Name

Dell Products L.P.

Inventor(s)

Shashikiran Rajagopal of Bengaluru (IN)

Alla Bharath of Andhra Pradesh (IN)

G. Madhanmohan Reddy of Andrapradesh (IN)

Krishna Mohan Akkinapalli of Leander TX (US)

Bijan Kumar Mohanty of Austin TX (US)

Hung T. Dinh of Austin TX (US)

Satish Ranjan Das of Round Rock TX (US)

TOPOLOGY EXPLORER FOR MESSAGE-ORIENTED MIDDLEWARE USING MACHINE LEARNING TECHNIQUES - A simplified explanation of the abstract

This abstract first appeared for US patent application 18121252 titled 'TOPOLOGY EXPLORER FOR MESSAGE-ORIENTED MIDDLEWARE USING MACHINE LEARNING TECHNIQUES

The patent application describes methods, apparatus, and processor-readable storage media for implementing topology explorers for message-oriented middleware using machine learning techniques.

• Obtaining data related to messaging topologies and message-oriented middleware.
• Predicting anomalies in messaging topologies using machine learning techniques.
• Recommending alternate messaging topologies based on predicted anomalies.
• Performing automated actions based on predicted anomalies and recommended alternate topologies.

Potential Applications: This technology can be applied in various industries utilizing message-oriented middleware systems, such as telecommunications, finance, and healthcare.

Problems Solved: This technology addresses the challenge of identifying anomalies in messaging topologies and provides solutions to optimize messaging systems.

Benefits: The technology enables proactive anomaly detection, improves system performance, and enhances overall efficiency in message-oriented middleware environments.

Commercial Applications: "Machine Learning Techniques for Message-Oriented Middleware Topology Optimization" - This technology can be utilized by software companies offering middleware solutions to enhance the performance and reliability of messaging systems.

Prior Art: Researchers and developers can explore existing literature on machine learning applications in middleware systems to understand the evolution of this technology.

Frequently Updated Research: Stay informed about advancements in machine learning algorithms for anomaly detection in messaging systems to leverage the latest innovations in the field.

Questions about Machine Learning Techniques for Message-Oriented Middleware Topology Optimization: 1. How does this technology improve the efficiency of message-oriented middleware systems? 2. What are the key factors to consider when recommending alternate messaging topologies based on predicted anomalies?

Original Abstract Submitted

Methods, apparatus, and processor-readable storage media for implementing topology explorers for message-oriented middleware using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one messaging topology associated with at least one message-oriented middleware; predicting one or more anomalies associated with the at least one messaging topology by processing at least a portion of the obtained data using a first set of one or more machine learning techniques; recommending one or more alternate messaging topologies associated with the at least one message-oriented middleware by processing at least a portion of the one or more predicted anomalies and at least a portion of the obtained data using a second set of one or more machine learning techniques; and performing one or more automated actions based on the one or more predicted anomalies and/or the one or more recommended alternate messaging topologies.