Project Log Process Mining for Actionable Ticket Prioritization
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Solution Overview
Problem
Large enterprises face challenges in effectively utilizing vast amounts of log data due to its volume and complexity, leading to logistical difficulties in managing projects and processes, with existing solutions often being difficult for end users to implement.
Innovation Solution
A computerized method involving process mining of user log data, translation of results into actionable insights using a machine learning engine, and conversion into tickets for a product management team, with prioritization using a classification machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If process mining is performed on large volumes of log data, then actionable insights are generated, but the complexity and difficulty of implementation increases
Solution Approach 1:
The patent introduces an automated processing system that acts as an intermediary between raw log data and actionable insights. This system includes components for automated log collection, parsing, process mining execution, and result visualization, which mediates the complex transformation process and makes it accessible to users without requiring deep expertise in process mining techniques.
Solution Approach 2:
The system enables self-service by automatically performing data collection, processing, and analysis without requiring manual intervention. The automated generation of process maps, bottleneck identification, and insight creation allow the system to serve itself in transforming raw data into actionable information, reducing the need for specialized human expertise.
2Loss of information
If manual analysis of log data is performed, then insights can be generated, but time consumption and productivity are reduced
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated computational systems. Instead of human analysts manually examining log data, the system uses automated parsers, process mining algorithms, and visualization tools to perform the analysis, dramatically increasing productivity while maintaining or improving insight quality.
Solution Approach 2:
The system enables continuous automated analysis of log data, allowing insights to be generated continuously rather than through intermittent manual analysis. The automated pipeline can process incoming log data in real-time or near-real-time, maintaining continuous useful action in the analysis process and significantly improving overall productivity.
3Loss of information
If existing process mining tools are used, then log data can be processed, but ease of operation is reduced due to high entry barrier
Solution Approach 1:
The patent introduces an intermediary layer that translates complex process mining operations into user-friendly interfaces and visualizations. This intermediary system handles the complexity of data processing, parsing, and analysis in the background while presenting simplified results and insights to users, making the system accessible to non-experts.
Solution Approach 2:
The system creates simplified copies or representations of complex process mining results through visualizations, process maps, and automated insights. Instead of requiring users to directly interpret complex raw data and technical outputs, the system generates accessible copies in the form of visual process flows, bottleneck identifications, and actionable recommendations that are easy to understand and operate with.
Data Source
AI summary
Computer systems, apparatuses, processors, and non-transitory computer-readable storage devices configured for executing a method comprising performing process mining on user log data; translating results of the process mining into actionable insights using a machine learning engine; and providing an option to convert the actionable insights into one or more tickets for a product management team.