dc.creator | García García, Julián Alberto | es |
dc.creator | Arévalo Maldonado, Carlos | es |
dc.creator | Meidan, Ayman | es |
dc.creator | Morillo Baro, Esteban | es |
dc.creator | Escalona Cuaresma, María José | es |
dc.date.accessioned | 2021-11-09T11:56:26Z | |
dc.date.available | 2021-11-09T11:56:26Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | García García, J.A., Arévalo Maldonado, C., Meidan, A., Morillo Baro, E. y Escalona Cuaresma, M.J. (2021). gPROFIT: A Tool to Assist the Automatic Extraction of Business Knowledge From Legacy Information Systems. IEEE Access, 9, 94934-94952. | |
dc.identifier.issn | 2169-3536 | es |
dc.identifier.uri | https://hdl.handle.net/11441/127201 | |
dc.description.abstract | Business digitization is a crucial strategy for business growth in the 21st century. Its bene ts
include improving business process automation, customer satisfaction, productivity, decision-making,
turnover, and adaptation to market changes. However, digitization is not a trivial task. As a major paradigm
and mindset shift, it involves a lot of effort within an organization and therefore requires commitment from
employees and managers. This is especially critical in companies whose business processes are mostly reliant
on legacy information systems (LIS), which are usually specialized and based on technological architectures
that could be considered obsolete. The replacement of these systems by more recent, process-oriented
technologies, the building up of employees' know-how and the continued use of outdated documentation are
dif cult, expensive tasks that hinder the initiation of continuous improvement processes in companies. This
paper proposes techniques for nding and extracting process models from legacy databases. Speci cally,
it (i) lays the theoretical foundations of a model-driven framework for systematically extracting business
process models (conform to standard BPMN notation) from LIS considering process time perspective, and
(ii) proposes a technological tool called gPROFIT, which uses machine learning techniques to support that
theoretical framework, facilitate its use in real environments and extract the business knowledge embedded in
such legacy systems. The paper also presents proofs-of-concept showing howour proposal has been validated
in several legacy systems. | es |
dc.description.sponsorship | Agencia Estatal de Investigación PID2019-105455GB-C31/AEI/ 10.13039/501100011033 | es |
dc.format | application/pdf | es |
dc.format.extent | 19 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Access, 9, 94934-94952. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Business digitization | es |
dc.subject | Model-driven engineering | es |
dc.subject | Process mining | es |
dc.subject | Legacy information systems | es |
dc.subject | Machine learning techniques | es |
dc.title | gPROFIT: A Tool to Assist the Automatic Extraction of Business Knowledge From Legacy Information Systems | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | 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/AEI/ 10.13039/501100011033 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9468657 | es |
dc.identifier.doi | 10.1109/ACCESS.2021.3093356 | es |
dc.journaltitle | IEEE Access | es |
dc.publication.volumen | 9 | es |
dc.publication.initialPage | 94934 | es |
dc.publication.endPage | 94952 | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |