Publication
CASE ID DETECTION IN UNLABEL LED EVENT LOGS FOR PROCESS MINING
| datacite.subject.fos | Engenharia e Tecnologia::Outras Engenharias e Tecnologias | pt_PT |
| dc.contributor.advisor | Rijo, Rui Pedro Charters Lopes | |
| dc.contributor.advisor | Martinho, Ricardo Filipe Gonçalves | |
| dc.contributor.advisor | Grilo, Carlos Fernando de Almeida | |
| dc.contributor.author | Vicente, André Alexandre dos Santos | |
| dc.date.accessioned | 2024-02-08T11:58:51Z | |
| dc.date.available | 2024-02-08T11:58:51Z | |
| dc.date.issued | 2023-12-04 | |
| dc.description.abstract | In the realm of data science, event logs serve as valuable sources of information, capturing sequences of events or activities in various processes. However, when dealing with unlabelled event logs, the absence of a designated Case ID column poses a critical challenge, hindering the understanding of relationships and dependencies among events within a case or process. Motivated by the increasing adoption of data-driven decision-making and the need for efficient data analysis techniques, this master’s project presents the "Case ID Column Identification Library" project. This library aims to streamline data preprocessing and enhance the efficiency of subsequent data analysis tasks by automatically identifying the Case ID column in unlabelled event logs. The project’s objective is to develop a versatile and user-friendly library that incorporates multiple methods, including a Convolutional Neural Network (CNN) and a parameterizable heuristic approach, to accurately identify the Case ID column. By offering flexibility to users, they can choose individual methods or a combination of methods based on their specific requirements, along with adjusting heuristic-based formula coefficients and settings for fine-tuning the identification process. This report presents a comprehensive exploration of related work, methodology, data understanding, methods for Case ID column identification, software library development, and experimental results. The results demonstrate the effectiveness of the proposed methods and their implications for decision support systems. | pt_PT |
| dc.identifier.tid | 203526180 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.8/9410 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Process Mining | pt_PT |
| dc.subject | CNN | pt_PT |
| dc.subject | Case ID Identification | pt_PT |
| dc.subject | Attribute Identification | pt_PT |
| dc.title | CASE ID DETECTION IN UNLABEL LED EVENT LOGS FOR PROCESS MINING | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados | pt_PT |
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