Towards Immersive Process Simulation for Declarative Models - Accompanying Material

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López, Hugo-Andrés;Jensen, Simon James

Description

This dataset contains the research protocol, transcripts and codings for the paper “Towards Immersive Process Simulation for Declarative Models.” Accepted for publication at the International Conference on Business Process Management - Forum. 2024. The documents are ordered by number. We describe their contents below: 1. Appendix A - Consent Form used in research and validation interviews in section 4 and section 62. Appendix B - DCR Training Material used to familiarize the research participants with DCR in section 43. Appendix C - Elicitation Interview Script & Validation Session Interview Protocol used in sections 4 and 64. Appendix D - First round of coding of Elicitation interviews5. Appendix E - Second round of coding of Elicitation interviews6. Appendix F - Third round of coding of Elicitation interviews7. Appendix G - First round of coding of Validation interviews8. Appendix H - Second round of coding of Validation interviews9. Appendix I1 - Third round of coding of Validation interviews, part 110. Appendix I2 - Third round of coding of Validation interviews, part 211. Appendix J - Validation participant DCR graph representation preferences: questionnaire and answersMoreover, we included all the transcripts of the interviews in raw form. The file transcript_ideation.pdf contains the transcripts of the requirement elicitation interviews conducted during the research phase.The file transcript_validation.pdf contains the the transcripts of the validation interviews. The github repository https://github.com/GloriousHypnotoad/DCR-Graph-artifact contains the source code of the software artifact used in the paper.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.5

FAIR Score

73%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

Creative Commons Attribution 4.0 International

Assigned Domain

Subfield

Artificial Intelligence

Field

Computer Science

Domain

Physical Sciences

Confidence Score

39%

Source

Scholar Data Model

Normalization Factors

FT

51.92

CTw

1.00

MTw

1.00