Predicting remaining time for an energy regulator’s highly parallel process
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Bond, Corey
issued: 2023-06-08T19:01:40Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Business process mining is a field that allows businesses to leverage data collected by business systems to gain insight into their processes. While process mining is a broad field, this thesis concentrates on process duration prediction based on information gained by reviewing historical data. Specifically, we investigate a provincial energy regulator's permit application process, and explore methods to predict the duration of the application, along with one of the sub-processes. The primary concern is developing an interpretable model to support the regulatory body in explaining the results of the predictions. Our goals are to solve the problem of how long a permit application might take to process, and support engagement with local First Nations communities by providing accurate estimates of the consultation process. These predictions are complicated by the highly complex processes being predicted, which include parallel execution of process tasks, causing significant data complexity. We solve the challenge of predicting a highly complex process by bridging the gap between traditional process mining and machine learning methods, while focusing on an interpretable and explainable model. We modify a state of the art random forest method called eXplainable Reasonably Randomized Forest (XRRF) by implementing a comprehensive data cleaning and engineering strategy. This modification also extends the feature selection method that provides interpretability of the results, and increases the hyperparameters available to improve the model's accuracy. In this thesis, we present the modified method, called Extended eXplainable Reasonably Randomized Forest (EXRRF). With this method, we were able to identify the factors that have the greatest impact on the duration of the consultation and application processes, while maintaining accuracy within 29-35% of the mean duration for each of these highly complex and variable processes.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84821
note: Predicting remaining time for anenergy regulator’s highly parallelprocessbyCorey BondB.Sc. Hons., The University of British Columbia, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)June 2023© Corey Bond, 2023The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Predicting remaining time for an energy regulator’s highly parallel processsubmitted by Corey Bond in partial fulfilment of the requirements of thedegree of Master of SciencePatricia Lasserre, Irving K. Barber Faculty of ScienceSupervisorYves Lucet, Irving K. Barber Faculty of ScienceCo-SupervisorIfeoma Adaji, Irving K. Barber Faculty of ScienceSupervisory Committee MemberMohamed Shehata, Irving K. Barber Faculty of ScienceSupervisory Committee MemberChen Feng, Faculty of Applied ScienceUniversity ExamineriiAbstractBusiness process mining is a field that allows businesses to leverage datacollected by business systems to gain insight into their processes. Whileprocess mining is a broad field, this thesis concentrates on process durationprediction based on information gained by reviewing historical data. Specif-ically, we investigate a provincial energy regulator’s permit application pro-cess, and explore methods to predict the duration of the application, alongwith one of the sub-processes. The primary concern is developing an inter-pretable model to support the regulatory body in explaining the results ofthe predictions.Our goals are to solve the problem of how long a permit applicationmight take to process, and support engagement with local First Nationscommunities by providing accurate estimates of the consultation process.These predictions are complicated by the highly complex processes beingpredicted, which include parallel execution of process tasks, causing signif…
has type: Thesis/Dissertation
date issued: 2023-09
is shown at: 10.14288/1.0433098
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Lasserre, P.
contributor: Lucet, Yves
type: Text
identifier URI: http://hdl.handle.net/2429/84821