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Enhanced Variable Functionality

Overview: The proposed feature enhancements aim to elevate the flexibility and usability of variable management within the ARIS Process Mining analyses script. By making variables non-static, enabling variable population through queries, and persisting variable values, users can enjoy enhanced customization, automation, and convenience in their analysis apps.

Feature Enhancements:

1. Make Variables Non-Static:

   • Currently, variables within the ARIS Process Mining analyses script are static, limiting adaptability and dynamism. By making variables non-static, users can dynamically adjust variable values during analysis execution, thereby enhancing flexibility and enabling real-time customization.

2. Fill Variables with Queries:

   • Set Default Variables:

     • Introduce the capability to populate variables with default values derived from predefined queries. This allows users to establish initial variable states, facilitating streamlined analysis setup.

   • Fill Variables Based on Query Results:

     • Enable the population of variables based on the results of user-defined queries. By dynamically filling variables with query outputs, users can automate variable assignment, optimizing analysis efficiency and accuracy. For example this will enable populating default variable values with values uploaded through a CSV

   • Defaults for Filters - When Nothing is Clicked:

     • Implement default values for filters when no specific selections are made. This ensures continuity in analysis execution by providing fallback values for unselected variables, enhancing user experience and reducing manual intervention. It prevents Analysis app to open without showing any result

3. Make Variables Available for Display Name for a Filter:

   • Integrate the functionality to utilize variables as display names for filter components. By associating variables with filter labels, users can enhance clarity and contextuality in analysis visualization, fostering better comprehension and interpretation of results.

4. Persist Variable Values:

   • Address the inconvenience of reselecting variable values by enabling the persistence of variable states across analysis sessions. By retaining variable values between sessions, users can seamlessly resume analysis workflows without the need for repetitive configuration, thereby improving productivity and user satisfaction.

Benefits:

• Enhanced Flexibility: Non-static variables empower users to adapt analysis parameters dynamically, accommodating evolving analytical requirements.

• Automation: Query-based variable population automates variable assignment, streamlining analysis setup and execution.

• User Convenience: Persistence of variable values eliminates the need for repetitive selection, enhancing user experience and efficiency.

• Clarity and Context: Utilizing variables as filter display names improves the interpretability of analysis results, fostering better insights and decision-making.

Implementation Considerations:

• Ensure compatibility with existing ARIS Process Mining functionalities and interfaces.

• Provide intuitive user interfaces for configuring variable settings and queries.

• Validate the robustness and efficiency of query-based variable population mechanisms.

• Implement secure storage mechanisms for persisted variable values to maintain data integrity and confidentiality.

Conclusion: The proposed feature enhancements for ARIS Process Mining analyses script aim to elevate variable management capabilities, empowering users with greater flexibility, automation, and convenience in their analytical endeavors. By incorporating these enhancements, ARIS Process Mining can further solidify its position as a leading solution for process analysis and optimization.