Vector Based Modelling of Business Processes

Virginia Niculescu, Maria-Camelia Chisăliţă-Creţu, Cristina-Claudia Osman, Adrian Sterca

2024

Abstract

Robotic Process Automation (RPA) platforms target the automation of repetitive tasks belonging to business processes, performed by human users. We are trying to increase the level of abstraction in representing complex processes (made from several conceptual operations) for RPA, by using vectors that allow not only a simple and condensed modelling, but also an efficient way towards obtaining an optimal execution order for them. Vector-based representation of the processes can serve to optimize the user-specified execution order of the conceptual operations that constitute a process. For this, we propose an optimization strategy based on a heuristic that helps us to rearrange the conceptual operations efficiently, thus reducing the total execution time of the process.

Paper Citation

in Harvard Style

Niculescu V., Chisăliţă-Creţu M., Osman C. and Sterca A. (2024). Vector Based Modelling of Business Processes. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-696-5, SciTePress, pages 735-742. DOI: 10.5220/0012739100003687

in Bibtex Style

@conference{enase24,
author={Virginia Niculescu and Maria-Camelia Chisăliţă-Creţu and Cristina-Claudia Osman and Adrian Sterca},
title={Vector Based Modelling of Business Processes},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2024},
pages={735-742},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012739100003687},
isbn={978-989-758-696-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Vector Based Modelling of Business Processes
SN - 978-989-758-696-5
AU - Niculescu V.
AU - Chisăliţă-Creţu M.
AU - Osman C.
AU - Sterca A.
PY - 2024
SP - 735
EP - 742
DO - 10.5220/0012739100003687
PB - SciTePress