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Process Discovery in Project Managem...

Romero, Jose Alejandro Briones,

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Process Discovery in Project Management: Process and Sequence Mining Approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Process Discovery in Project Management: Process and Sequence Mining Approaches // Jose Alejandro Briones Romero.
Author:	Romero, Jose Alejandro Briones,
Description:	1 electronic resource (80 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-04.
Contained By:	Masters Abstracts International86-04.
Subject:	Data integrity. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31615535
ISBN:	9798342309745

Process Discovery in Project Management: Process and Sequence Mining Approaches /
Romero, Jose Alejandro Briones,

Process Discovery in Project Management: Process and Sequence Mining Approaches /Jose Alejandro Briones Romero. - 1 electronic resource (80 pages)

Source: Masters Abstracts International, Volume: 86-04.

Process mining and data mining practices have discovered knowledge that allows companies to manage resources efficiently. The current thesis work relies on process mining and sequential pattern mining implementations from event logs obtained of Strongstep certification projects. The objective is to extract, update and recommend a standard process for nine project phases of CMMI, ISO 27001, and GDPR projects. After collecting the data, a data understanding and data preparation phase occur to execute cleaning tasks and guarantee model robustness. The process discovery implementation of process mining uses the Inductive Miner infrequent (IMi) of PM4Py library to generate the correspondent model through Petri nets and Directly-Follow Graphs. Then, all the process models are assessed with four quality metrics: fitness, precision, simplicity, and generalization. As every phase process is understood as a set of activities without parallel occurrences, a sequential pattern mining approach is executed to support the results of the first technique. This implementation uses the CM-SPAM algorithm of the SPMF tool to get the most frequent pattern for each phase. The generated outcome defined six standard phase processes that Strongstep can use for new project instances. The data was highly variable for the other three phases, which led to the generation of inconsistent models and the lack of frequent pattern recommendations. Both process and sequence mining techniques showed their effectiveness in real scenarios. Nevertheless, there are deployment and optimization tasks that can be realized in future work.

English

ISBN: 9798342309745Subjects--Topical Terms:

2142314
Data integrity.

Process Discovery in Project Management: Process and Sequence Mining Approaches /
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245 1 0 $a Process Discovery in Project Management: Process and Sequence Mining Approaches / $c Jose Alejandro Briones Romero.
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500 $a Source: Masters Abstracts International, Volume: 86-04.
500 $a Advisors: Moreira, Joao Mendes; Sousa, Ricardo Teixeira.
502 $b M.Eng. $c Universidade do Porto (Portugal) $d 2021.
520 $a Process mining and data mining practices have discovered knowledge that allows companies to manage resources efficiently. The current thesis work relies on process mining and sequential pattern mining implementations from event logs obtained of Strongstep certification projects. The objective is to extract, update and recommend a standard process for nine project phases of CMMI, ISO 27001, and GDPR projects. After collecting the data, a data understanding and data preparation phase occur to execute cleaning tasks and guarantee model robustness. The process discovery implementation of process mining uses the Inductive Miner infrequent (IMi) of PM4Py library to generate the correspondent model through Petri nets and Directly-Follow Graphs. Then, all the process models are assessed with four quality metrics: fitness, precision, simplicity, and generalization. As every phase process is understood as a set of activities without parallel occurrences, a sequential pattern mining approach is executed to support the results of the first technique. This implementation uses the CM-SPAM algorithm of the SPMF tool to get the most frequent pattern for each phase. The generated outcome defined six standard phase processes that Strongstep can use for new project instances. The data was highly variable for the other three phases, which led to the generation of inconsistent models and the lack of frequent pattern recommendations. Both process and sequence mining techniques showed their effectiveness in real scenarios. Nevertheless, there are deployment and optimization tasks that can be realized in future work.
520 $a Process mining e data mining sao metodologias que permitem ajudar as empresas a gerir os recursos de forma eficiente. O trabalho desta tese baseia-se em implementacoes de process mining e sequential pattern mining a partir de registos de eventos obtidos de projetos de certificacao da Strongstep. O objetivo e extrair, atualizar e recomendar um processo padrao para nove fases de projetos de CMMI, ISO 27001, e RGPD. Apos a recolha dos dados, ocorre uma fase de compreensao e preparacao dos dados para executar tarefas de limpeza e garantir a robustez do modelo. A implementacao de process discovery do process mining utiliza o Inductive Miner infrequent (IMi) da biblioteca PM4Py para obter o modelo correspondente representado em Petri nets e DirectlyFollow Graphs. Posteriormente, todos os modelos do processo sao avaliados com quatro metricas: fitness, precisao, simplicidade e generalizacao. Como cada processo de fase e entendido como um conjunto de atividades sem ocorrencias paralelas, e executada uma abordagem de sequential pattern mining para apoiar os resultados da primeira tecnica. Esta implementacao utiliza o algoritmo CM-SPAM da ferramenta SPMF para obter o padrao mais frequente para cada fase. O resultado gerado definiu seis processos padrao que Strongstep pode utilizar para novas instancias do projeto. Os dados foram altamente variaveis para as outras tres fases, o que levou a geracao de modelos inconsistentes e a falta de recomendacoes de padroes frequentes. Tanto as tecnicas de process mining como as de sequential pattern mining mostraram a sua eficacia em cenarios reais. No entanto, ha tarefas de implantacao e otimizacao que podem ser realizadas em trabalhos futuros.
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