Beyond Directly‑Follows Graphs: Graphical Modelling in Process Mining - Dataset

This dataset contains detailed information from a Systematic Literature Review (SLR) on Process Mining, including bibliographic data, methodologies, and key findings from selected research papers.

This study presents a Systematic Literature Review (SLR) on Process Mining, aiming to analyze the evolution, trends, and challenges in the field. By applying a structured search strategy across multiple digital libraries, we identify, classify, and synthesize relevant research works. The study provides insights into methodologies, application domains, and emerging directions in process mining, offering a comprehensive overview for researchers and practitioners.

The repository contains the following files:

01_DataExtraction.xlsx: provides a structured overview of the study process. It includes a brief description of the dataset, the systematic data extraction for all GPS entries, and a quality assessment to evaluate the reliability of the extracted information. Additionally, it contains a short analysis of each selected GPS, offering a concise yet comprehensive perspective on the main findings.

02_Search_Query_dataset.xlsm: provides a comprehensive record of the studies retrieved from digital libraries, detailing which articles were selected for analysis and which were excluded based on predefined criteria. This dataset ensures transparency in the selection process and facilitates reproducibility in systematic literature reviews.

03_Groups_of_Primary_Studies.xlsm: categorizes independent studies in process mining, organizing them based on their authorship, title, year, and publication source. This classification ensures a structured overview of relevant research, facilitating the evaluation and comparison of contributions within the field.

04_Answer_QA.xlsm: presents a structured assessment of the methodological quality of selected studies, evaluating key criteria to determine their rigor, reliability, and applicability in process mining research.