Matrix of guidelines to improve the understandability of non-expert users in process mining projects

Descripción del Articulo

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
Detalles Bibliográficos
Autores: Teran, Bryhan Chise, Bravo, Jimmy Manuel Hurtado, Armas-Aguirre, Jimmy, Mayorga, Santiago Aguirre
Formato: artículo
Fecha de Publicación:2020
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/656575
Enlace del recurso:http://hdl.handle.net/10757/656575
Nivel de acceso:acceso embargado
Materia:Color Psychology
Data Visualization
Guidelines
Matriz
Methodology
Process Mining
Visual Analytics
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network_acronym_str UUPC
network_name_str UPC-Institucional
repository_id_str 2670
dc.title.en_US.fl_str_mv Matrix of guidelines to improve the understandability of non-expert users in process mining projects
title Matrix of guidelines to improve the understandability of non-expert users in process mining projects
spellingShingle Matrix of guidelines to improve the understandability of non-expert users in process mining projects
Teran, Bryhan Chise
Color Psychology
Data Visualization
Guidelines
Matriz
Methodology
Process Mining
Visual Analytics
title_short Matrix of guidelines to improve the understandability of non-expert users in process mining projects
title_full Matrix of guidelines to improve the understandability of non-expert users in process mining projects
title_fullStr Matrix of guidelines to improve the understandability of non-expert users in process mining projects
title_full_unstemmed Matrix of guidelines to improve the understandability of non-expert users in process mining projects
title_sort Matrix of guidelines to improve the understandability of non-expert users in process mining projects
author Teran, Bryhan Chise
author_facet Teran, Bryhan Chise
Bravo, Jimmy Manuel Hurtado
Armas-Aguirre, Jimmy
Mayorga, Santiago Aguirre
author_role author
author2 Bravo, Jimmy Manuel Hurtado
Armas-Aguirre, Jimmy
Mayorga, Santiago Aguirre
author2_role author
author
author
dc.contributor.author.fl_str_mv Teran, Bryhan Chise
Bravo, Jimmy Manuel Hurtado
Armas-Aguirre, Jimmy
Mayorga, Santiago Aguirre
dc.subject.en_US.fl_str_mv Color Psychology
Data Visualization
Guidelines
Matriz
Methodology
Process Mining
Visual Analytics
topic Color Psychology
Data Visualization
Guidelines
Matriz
Methodology
Process Mining
Visual Analytics
description El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2021-06-23T13:49:50Z
dc.date.available.none.fl_str_mv 2021-06-23T13:49:50Z
dc.date.issued.fl_str_mv 2020-06-01
dc.type.en_US.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.issn.none.fl_str_mv 21660727
dc.identifier.doi.none.fl_str_mv 10.23919/CISTI49556.2020.9140823
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/656575
dc.identifier.eissn.none.fl_str_mv 21660735
dc.identifier.journal.en_US.fl_str_mv Iberian Conference on Information Systems and Technologies, CISTI
dc.identifier.eid.none.fl_str_mv 2-s2.0-85089035317
dc.identifier.scopusid.none.fl_str_mv SCOPUS_ID:85089035317
dc.identifier.isni.none.fl_str_mv 0000 0001 2196 144X
identifier_str_mv 21660727
10.23919/CISTI49556.2020.9140823
21660735
Iberian Conference on Information Systems and Technologies, CISTI
2-s2.0-85089035317
SCOPUS_ID:85089035317
0000 0001 2196 144X
url http://hdl.handle.net/10757/656575
dc.language.iso.en_US.fl_str_mv eng
language eng
dc.relation.url.en_US.fl_str_mv https://ieeexplore.ieee.org/document/9140823
dc.rights.en_US.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.en_US.fl_str_mv application/html
dc.publisher.en_US.fl_str_mv IEEE Computer Society
dc.source.es_PE.fl_str_mv Repositorio Academico - UPC
Universidad Peruana de Ciencias Aplicadas (UPC)
dc.source.none.fl_str_mv reponame:UPC-Institucional
instname:Universidad Peruana de Ciencias Aplicadas
instacron:UPC
instname_str Universidad Peruana de Ciencias Aplicadas
instacron_str UPC
institution UPC
reponame_str UPC-Institucional
collection UPC-Institucional
dc.source.journaltitle.none.fl_str_mv Iberian Conference on Information Systems and Technologies, CISTI
dc.source.volume.none.fl_str_mv 2020-June
bitstream.url.fl_str_mv https://repositorioacademico.upc.edu.pe/bitstream/10757/656575/1/license.txt
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio académico upc
repository.mail.fl_str_mv upc@openrepository.com
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spelling 2989895103cac549cf14a42a5928f55fccf7e20e541569ce7c920a3597a44af84832ce656228b995761b32f4527dfa582f802d192022bb6a246b9f48b7f7b1d0Teran, Bryhan ChiseBravo, Jimmy Manuel HurtadoArmas-Aguirre, JimmyMayorga, Santiago Aguirre2021-06-23T13:49:50Z2021-06-23T13:49:50Z2020-06-012166072710.23919/CISTI49556.2020.9140823http://hdl.handle.net/10757/65657521660735Iberian Conference on Information Systems and Technologies, CISTI2-s2.0-85089035317SCOPUS_ID:850890353170000 0001 2196 144XEl texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.Process Mining is a discipline that recognizes three types of analysis: Discovery, monitoring, and process improvement. Organizations are focusing on redesigning and automating their major processes, according to a report published in 2018 [1]. In this way, a challenge n process mining is to show the results of the process analysis in a way that is understandable to non-expert users. Therefore, this research paper introduces a matrix of guidelines to guide process mining specialists/tool developers to improve the results of the analysis in process mining projects. This matrix is composed of 2 study fields that throughout the literature have been merging their virtues. First, process mining under 2 of its 3 types of projects: (1) based on objectives and (2) based on questions. The last type is based on data (exploratory analysis). Second, visualization of data with its techniques to represent data graphically. This research proposes a matrix of guidelines that integrates the discipline of process mining and the set of data visualization techniques based on the purpose of each graph (technique), the question / objective to be achieved and the importance that colors take in the analysis results in the process mining projects.Revisión por paresapplication/htmlengIEEE Computer Societyhttps://ieeexplore.ieee.org/document/9140823info:eu-repo/semantics/embargoedAccessRepositorio Academico - UPCUniversidad Peruana de Ciencias Aplicadas (UPC)Iberian Conference on Information Systems and Technologies, CISTI2020-Junereponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCColor PsychologyData VisualizationGuidelinesMatrizMethodologyProcess MiningVisual AnalyticsMatrix of guidelines to improve the understandability of non-expert users in process mining projectsinfo:eu-repo/semantics/articleLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/656575/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51false10757/656575oai:repositorioacademico.upc.edu.pe:10757/6565752021-06-23 13:49:50.779Repositorio académico upcupc@openrepository.comTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=
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Matrix of guidelines to improve the understandability of non-expert users in process mining projects
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