# A Tertiary Study for Process Mining
## 3 Results--Findings
### Data Synthesis  
#### 3.1.1 RQ1: Which Research Areas Are Addressed by SMSs/SLRs in Process Mining? All studies were classified according to their main area of concern. Both authors identified this by reading their titles, keywords, and abstracts. No major discrepancies were identified in the perceptions of both authors. Despite the small number of SMSs and SLRs in process mining, numerous research themes are explored by researchers, as indicated in Table 5. Most of the focus is on process mining areas (technologies and applications) and healthcare, as 72% of the included studies fall under these domains. Research interest is shown in other related information technology areas, such as agile software development, cybersecurity, etc. Having reviewed the literature for a suggested thematic analysis of PM [61], we concluded that there is no guidance for PM at the moment, other than the three areas of PM introduced in the PM Manifesto [1]. Therefore, we briefly present below some insights on the thematic areas addressed by the included studies. Process mining areas (technologies and applications): S2 constitutes a state-of-the-art analysis related to mining software processes. The authors focus on the analysis of research topics, data sources, and mining techniques and tools. The included studies are mapped based on the associated mining tools, identified correlations in research topics, and data sources used. Both S3 and S18 address PM topics in the area of education. S3 applies PM techniques in educational data and the synthesis is based on PM types and perspectives, PM tools, algorithms and analysis techniques, and methodologies used. S18 performs an analysis of studies applying PM in learning process design. The included studies are analyzed based on their research objectives, the PM technique, and the tools used. S4 is a secondary study that focuses on the event logs used for PM. Among others, the main classification dimensions used are model type and language, type of implementation, and type of evaluation data. In S8, the active research topics of PM and their main publishers are mapped. The studies are classified by the domains they explore or the industry segment used for PM. The most active research topics are associated with process discovery algorithms, conformance checking and architecture tools and improvements. Goal-oriented PM is examined in S11. The authors conclude that the use of PM in association with goals does not yet have a coherent line of research. S12 is a broad study on the field of PM that systematically assesses PM scenarios. More than 70% of the studies evidenced discovery as the main type of PM addressed. The study also analyzes the included studies by the technique used and the presented graphical results. S16 is the only study investigating PM in recommender systems. The authors provide a taxonomy of published studies in this field, based on the type and perspective of recommendations, a list of datasets and evaluation metrics used, implementation environments, and the algorithms used. A taxonomy consisting of 4 different approaches for addressing process complexity and the impact of contributing factors on PM results is the focus of the SLR undertaken in S19. Finally, S25 presents an SLR on process discovery and conformance-checking metrics for duplicate tasks. No classification or taxonomy of the primary studies included is present. The authors provide a comprehensive review of the current work to assist researchers in locating the right PM tools and metrics. Healthcare: Using the included primary studies, S1 creates a concept map and classification according to certain characteristics (type of research, contribution, healthcare specialty, and mining activity). This study is one of the two studies with the highest quality score (4.5 out of 5) included in this research and it was published as an open-access paper. The characteristics that comprised its overall quality score can be found in the Appendix A (Table 11).