Dell products l.p. (20240311655). TOPOLOGY EXPLORER FOR MESSAGE-ORIENTED MIDDLEWARE USING MACHINE LEARNING TECHNIQUES simplified abstract

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 20240311655 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, such as telecommunications, finance, and healthcare.

Problems Solved: This technology addresses the need for efficient anomaly detection and topology optimization in message-oriented middleware systems.

Benefits: Enhanced system performance, improved fault tolerance, and proactive maintenance of messaging topologies.

Commercial Applications: "Machine Learning-Based Topology Explorers for Message-Oriented Middleware" can be utilized by software companies developing middleware solutions for various industries.

Prior Art: Researchers can explore prior art related to anomaly detection in messaging systems, machine learning techniques in middleware optimization, and topology exploration tools.

Frequently Updated Research: Stay informed about the latest advancements in machine learning algorithms for anomaly detection and topology optimization in message-oriented middleware systems.

Questions about Machine Learning-Based Topology Explorers for Message-Oriented Middleware: 1. How does this technology improve the efficiency of message-oriented middleware systems? 2. What are the key challenges in implementing machine learning techniques for anomaly detection in messaging topologies?

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.