dc.creator | Martínez Rojas, Antonio | es |
dc.creator | Jiménez Ramírez, Andrés | es |
dc.creator | González Enríquez, José | es |
dc.creator | Reijers, Hajo A. | es |
dc.date.accessioned | 2022-11-14T12:25:08Z | |
dc.date.available | 2022-11-14T12:25:08Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Martínez Rojas, A., Jiménez Ramírez, A., González Enríquez, J. y Reijers, H.A. (2022). Analyzing Variable Human Actions for Robotic Process Automation. En BPM 2022: 20th International Conference on Business Process Management (75-90), Münster, Germany: Springer. | |
dc.identifier.isbn | 978-3-031-16102-5 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/139393 | |
dc.description.abstract | Robotic Process Automation (RPA) provides a means to
automate mundane and repetitive human tasks. Task Mining approaches
can be used to discover the actions that humans take to carry out a
particular task. A weakness of such approaches, however, is that they
cannot deal well with humans who carry out the same task differently
for different cases according to some hidden rule. The logs that are used
for Task Mining generally do not contain sufficient data to distinguish
the exact drivers behind this variability. In this paper, we propose a new
Task Mining framework that has been designed to support engineers who
wish to apply RPA to a task that is subject to variable human actions.
This framework extracts features from User Interface (UI) Logs that
are extended with a new source of data, namely screen captures. The
framework invokes Supervised Machine Learning algorithms to generate
decision models, which characterize the decisions behind variable human
actions in a machine-and-human-readable form. We evaluated the pro posed Task Mining framework with a set of synthetic UI Logs. Despite
the use of only relatively small logs, our results demonstrate that a high
accuracy is generally achieved. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades PID2019-105455GB-C31 (NICO) | es |
dc.description.sponsorship | Centro para el Desarrollo Tecnológico Industrial (CDTI) EXP 00130458/IDI-20210319-P018-20/E09 (CODICE) | es |
dc.format | application/pdf | es |
dc.format.extent | 16 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | BPM 2022: 20th International Conference on Business Process Management (2022), pp. 75-90. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Robotic process automation | es |
dc.subject | Process discovery | es |
dc.subject | Task mining | es |
dc.subject | Decision model discovery | es |
dc.title | Analyzing Variable Human Actions for Robotic Process Automation | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | PID2019-105455GB-C31 (NICO) | es |
dc.relation.projectID | EXP 00130458/IDI-20210319-P018-20/E09 (CODICE) | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-031-16103-2_8 | es |
dc.identifier.doi | 10.1007/978-3-031-16103-2_8 | es |
dc.contributor.group | Universidad de Sevilla. TIC-021: Engineering and Science for Software Systems | es |
dc.publication.initialPage | 75 | es |
dc.publication.endPage | 90 | es |
dc.eventtitle | BPM 2022: 20th International Conference on Business Process Management | es |
dc.eventinstitution | Münster, Germany | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Centro para el Desarrollo Tecnológico Industrial (CDTI) | es |