17809077. CLOSED LOOP VERIFICATION FOR EVENT GROUPING MECHANISMS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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CLOSED LOOP VERIFICATION FOR EVENT GROUPING MECHANISMS

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Pooja Aggarwal of Bengaluru (IN)

Harshit Kumar of Delhi (IN)

Amitkumar Manoharrao Paradkar of Mohegan Lake NY (US)

Rama Kalyani T. Akkiraju of Cupertino CA (US)

CLOSED LOOP VERIFICATION FOR EVENT GROUPING MECHANISMS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17809077 titled 'CLOSED LOOP VERIFICATION FOR EVENT GROUPING MECHANISMS

Simplified Explanation

The patent application describes a method and system for using a causal dependence graph of events in a large enterprise system to identify the most frequently used corrective action for a set of actions required by the enterprise.

  • The method utilizes a causal dependence graph to analyze the relationships between different events in the enterprise system.
  • The system correlates a set of actions or workflows with a set of corrective actions.
  • The method and system are designed to handle large sets of data related to actions performed by the enterprise system.
  • The goal is to determine the most frequently utilized corrective action for a given set of actions.

Potential Applications

This technology can be applied in various industries and sectors where large enterprise systems are used, such as:

  • Manufacturing: Identifying the most effective corrective actions for optimizing production processes.
  • IT Operations: Determining the most common solutions for resolving system issues and errors.
  • Customer Service: Finding the most frequently used corrective actions for addressing customer complaints or inquiries.
  • Supply Chain Management: Optimizing logistics and inventory management by identifying the most effective corrective actions.

Problems Solved

The technology addresses the following problems:

  • Difficulty in correlating a set of actions or workflows with the appropriate corrective actions in large enterprise systems.
  • Inefficient and time-consuming manual analysis of data to determine the most frequently utilized corrective actions.
  • Lack of a systematic approach to identify the most effective solutions for common issues or problems in enterprise systems.

Benefits

The use of this technology offers several benefits:

  • Improved efficiency in identifying and implementing corrective actions in enterprise systems.
  • Reduction in manual effort and time required for analyzing large sets of data.
  • Enhanced decision-making by utilizing a systematic approach based on causal dependence analysis.
  • Optimization of enterprise processes and workflows through the identification of frequently utilized corrective actions.


Original Abstract Submitted

A method and system is provided for utilizing a causal dependence graph of events in a large enterprise-related system to determine a most frequently utilized corrective action for a set of actions that the enterprise requires. Typically, with large sets of data related to actions that an enterprise system performs, it is non-trivial to correlate a set of actions (or workflows) with a set of corrective actions.

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