Abstract
Analyzing technical log data is essential for understanding user experience and identifying errors in employee interactions with applications. This process helps uncover bottlenecks impacting productivity and customer satisfaction.
Objectives:
1. Error Identification: A binary classification model was developed to detect application errors along the
customer journey.
2. Path Optimization: PM was used to detect inefficiencies in the employee pathway, enhancing interaction
with the application.
3. Efficiency Improvement: Eliminating errors to create a smoother and more productive user experience.
Process Stages:
1. Data Collection: Identifying log sources (servers, databases, client applications).
2. Data Processing and Aggregation: Filtering and organizing logs for analysis.
3. Data Analysis: Using PM and ML for error pattern detection.
4. Visualization: Creating dashboards to present key findings.
5. Implementation: Developing error-resolution plans and monitoring improvements.
6. Feedback and Updates: Gathering user feedback to enhance the process.
Hypotheses and Metrics: Several hypotheses were tested, such as the effect of high load times on error frequency, alongside metrics like Bounce Rate and Error Rate.
Process Mining Techniques Used:
1. Process Discovery: Mapping user interactions for error-prone steps.
2. Performance Analysis: Identifying delay points.
3. Clustering: Grouping sessions by behavior patterns.
4. Anomaly Detection: Identifying unusual events for focused attention.