TY - GEN
T1 - Matriz de lineamientos para mejorar el entendimiento de los usuarios no expertos en proyectos de minería de procesos
AU - Teran, Bryhan Chise
AU - Bravo, Jimmy Manuel Hurtado
AU - Armas-Aguirre, Jimmy
AU - Mayorga, Santiago Aguirre
N1 - Publisher Copyright:
© 2020 AISTI.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
KW - Color Psychology
KW - Data Visualization
KW - Guidelines
KW - Matriz
KW - Methodology
KW - Process Mining
KW - Visual Analytics
UR - http://www.scopus.com/inward/record.url?scp=85089035317&partnerID=8YFLogxK
U2 - 10.23919/CISTI49556.2020.9140823
DO - 10.23919/CISTI49556.2020.9140823
M3 - Contribución a la conferencia
AN - SCOPUS:85089035317
T3 - Iberian Conference on Information Systems and Technologies, CISTI
BT - Proceedings of CISTI 2020 - 15th Iberian Conference on Information Systems and Technologies
A2 - Rocha, Alvaro
A2 - Perez, Bernabe Escobar
A2 - Penalvo, Francisco Garcia
A2 - del Mar Miras, Maria
A2 - Goncalves, Ramiro
PB - IEEE Computer Society
T2 - 15th Iberian Conference on Information Systems and Technologies, CISTI 2020
Y2 - 24 June 2020 through 27 June 2020
ER -