Humboldt-Universität zu Berlin - Faculty of Mathematics and Natural Sciences - Process Management and Information Systems

Bachelor and Master Thesis

General Information

Our team offers bachelor and master thesis topics as well as student projects to be written in English.

Concerning the theses, there are two application windows in a year in which new topics are available. The first window is open from February 1st until April 1st. The second window is open from July 1st until October 1st. During these windows students can express their interest in a topic by sending an email Dr. Saimir Bala (firstname[dot]lastname[at]hu-berlin.de).

 

There are biannually info sessions where we explain the process of writing a thesis with our team. The next info session is scheduled for Februray 21st, 2024 [zoom link we be available soon].

 

The last info session took place on September 28th, 2023. Here you can find the slides (part I, part II) and recordings (part I, part II) of previous info sessions . 

  

Furthermore, find below a summary of guidelines for working on your thesis with us.

 

Process Overview

  • There are two main time windows in which the team proposes new topics: Feb 1st ­– Apr 1st and Jul 1st – Oct 1st
  • Within these windows students can apply for an open topic (see list of open topics below)
  • Application is done by sending an email to Dr. Saimir Bala (firstname[dot]lastname[at]hu-berlin.de).
  • We collect your applications and make a topic-student assignment in two rounds. First round on March, second round after the deadline.
  • Once a student has been matched to a supervisor, a meeting is scheduled to scope the topic.
  • Then, students must submit a research proposal to the supervisor within a month.
  • If the proposal is graded as passed, the supervision is officially registered
  • Once the thesis work is concluded, the thesis defense is scheduled within a dedicated defense slot.

 

Important Dates

09.02.2024: New topics released. Students can express their interest.

21.02.2024: Info session at 12:00 [Zoom link here]

01.03.2024: Topic assignment (1st round)

01.04.2024: Expression of interest deadline

08.04.2024: Topic assignment (2nd round)

08.05.2024: Research proposal submission

15.05.2024: Official start (if proposal sufficient)

 

Formatting

Please consider the following hints and guidelines for working on your thesis:

  • Templates for thesis and proposal: https://www.informatik.hu-berlin.de/de/studium/formulare/vorlagen
  • Page limits are as follows
    • page limit is for Bachelor Informatik 40 pages and for Kombibachelor Lehramt Informatik 30 pages
    • page limit is for Master Informatik 80 pages and for Master Information Systems 60 pages
  • The limits do not include cover, table of content, references, and appendices.

 

Prerequisites

The candidate is expected to be familiar with the general rules of writing a scientific paper. Some general references are helpful for framing any thesis, no matter which topic:

In agreement with the supervisor an individual list of expected readings should be studied by the student in preparation of the actual work on the thesis.

 

Grading

The grading of the thesis takes various criteria into account, relating both to the thesis as a product and the process of establishing its content. These include, but are not limited to:

  • Correctness of spelling and grammar
  • Aesthetic appeal of documents and figures
  • Compliance with formal rules
  • Appropriateness of thesis structure
  • Coverage of relevant literature
  • Appropriateness of research question and method
  • Diligence of own research work
  • Significance of research results
  • Punctuality of work progress
  • Proactiveness of handling research progress

 

Recent Topics

If you are interested in one of the following topics, please send an email expressing your interest to Dr. Saimir Bala (firstname[dot]lastname[at]hu-berlin.de). Please explain why this topic is interesting for you and how it fits your prior studies. Also explain what are your strengths in your studies and in which semester of your studies you are.

 

Topic 1: Actionable Recommendation for Learner in Learning Management System based on Process Mining (Bachelor/Master) 

This study explores the advantages of process mining in learning management systems to provide actionable recommendations to learners. By leveraging data-driven insights, it aims to enhance the learning experience by offering personalized guidance and suggestions to learners, ultimately improving their educational outcomes. This research delves into the potential benefits of process mining in the educational context, highlighting its capacity to empower learners on their educational journeys.

 

Initial References:

  • Wambsganss, Thiemo; Schmitt, Anuschka; Mahnig, Thomas; Ott, Anja; Soellner, Sigitai; Ngo, Ngoc Anh; and Geyer-Klingeberg, Jerome, "The Potential of Technology-Mediated Learning Processes: A Taxonomy and Research Agenda for Educational Process Mining" (2021). ICIS 2021 Proceedings. 1. https://aisel.aisnet.org/icis2021/diglearn_curricula/diglearn_curricula/1
  • AlQaheri, H.; Panda, M. An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students’ Learning Behavior and Improving Teaching Quality. Information 2022, 13, 29. https://doi.org/10.3390/info13010029
  • Bala, S., Revoredo, K., Mendling, J. (2023). Process Mining for Analyzing Open Questions Computer-Aided Examinations. In: Montali, M., Senderovich, A., Weidlich, M. (eds) Process Mining Workshops. ICPM 2022. Lecture Notes in Business Information Processing, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-27815-0_41

 

Supervisor: Rachmadita Andre Swari

 

Topic 2: Process discovery based on undesirable traces (Bachelor/Master)

Background: Process discovery techniques have been used to automatically learn a process model using observed traces (i.e., event logs). The traces are assumed correct and the final process model is expected to explain all the traces (i.e., desirable fitness of 1). However, the final model may also explain behaviors that are known by the specialist to be undesirable.

Research problem: The core research problem addressed is: How to learn a process model considering undesirable traces?

The aim is to propose a method to process discovery that learns using desirable and undesirable trace data.

Requirements: The candidate must have previous knowledge of process mining and software development. Further desirable requirements are pro-activity and self-organization.

Initial Reference

  • Revoredo, K.: On the use of domain knowledge for process model repair. Softw. Syst. Model. (2022)

 

Supervisor: Kate Cerqueira Revoredo

 

Topic 3: Context-aware process monitoring (Bachelor/Master)

Background: Business process monitoring is one of the phases of the BPM cycle concerned with extracting insights from the execution of a process. The digitization of the processes of an organisation has made available a vast amount of trace data about the execution of these processes, which allows for the use of data-driven process monitoring techniques. Given that, in many situations, it is not enough to just directly use the activities information present in the trace data of the process to achieve an accurate output, recent approaches have considered other sources of information combined with activities information, such as sensors data, or domain knowledge. However,, in most situations, additional data is used in a non-systematic way.


Research problem: The core research problem addressed is: How can contextual data be used for process monitoring?
The aim is to propose a method to process monitoring using contextual data.

Requirements: The candidate must have previous knowledge of process mining and software development. Further desirable requirements are pro-activity and self-organization.

Initial References:

  • da Cunha Mattos, T., Santoro, F.M., Revoredo, K., Nunes, V.T.: A formal representation for context-aware business processes. Computers in Industry 65(8) (2014) 1193–1214
  • Chamorro, A.E.M., Revoredo, K., Resinas, M., del-R ́ıo-Ortega, A., Santoro, F.M., Ruiz-Cort ́es, A.: Context-aware process performance indicator prediction. IEEE Access 8 (2020) 222050–222063
  • Bayomie, D., Revoredo, K., Mendling, J.: Multi-perspective process analysis: Mining the association between control flow and data objects. In: CAiSE. Volume 13295 of Lecture Notes in Computer Science., Springer (2022) 72–89

 

Supervisor: Kate Cerqueira Revoredo

 

Topics 4: Uses of Models in Agile Software Development (Bachelor/Master)

Motivation & problem: Modeling is a key topic in software engineering. In software development projects, among other aspects, modeling supports the developer in understanding the design by providing an overview and a tool for communication with fellow developers and other stakeholders. The benefits of models for supporting system analysis and design activities have been highlighted regarding their cognitive effectiveness, often in the context of traditional methodologies. However, these benefits have also been discussed in the agile scene, but it is still not clear to what extent models are used in agile software development projects.

Objectives: conduct a systematic review of the literature, identify the uses of models in agile software development, categorize and prioritize them, and propose a framework to support agile software development based on these findings. The findings shall be evaluated according to the perspective of practitioners.

Prerequisites: (1) Basic knowledge of agile software development methodologies; (2) Intermediate knowledge of models used in software development; (3) Pro-activity, self-organization, attention to detail (desirable).

 

Initial References:

  • Ambler, Scott W. The object primer: Agile model-driven development with UML 2.0. Cambridge University Press, 2004.
  • Alfraihi, Hessa Abdulrahman A., and Kevin Charles Lano. "The integration of agile development and model driven development: A systematic literature review." The 5th International Confrence on Model-Driven Engineeing and Software Development (2017).
  • Wagner, Stefan, Daniel Méndez Fernández, Michael Felderer, Antonio Vetrò, Marcos Kalinowski, Roel Wieringa, Dietmar Pfahl et al. "Status quo in requirements engineering: A theory and a global family of surveys." ACM Transactions on Software Engineering and Methodology (TOSEM) 28, no. 2 (2019): 1-48.
  • Petre, Marian. "UML in practice." In 2013 35th international conference on software engineering (icse), pp. 722-731. IEEE, 2013.

 

Supervisor: Cielo González Moyano

 

Topic 5: Artificial Intelligence in Project Management for Software Development Projects (Master)

Motivation & problem: Artificial intelligence is applied in software engineering management for taking decisions, estimating, managing technical debt, and planning, just to provide some examples. These applications have been widely studied by researchers. However, there is no study that presents a deep overview of how artificial intelligence is used for management activities in software development projects. Given the rising interest in artificial intelligence and the need of optimizing management in software projects, having a holistic overview can potentially be beneficial for practitioners and researchers.

Objectives: conduct a systematic review of the literature to identify the status quo on the topic. The findings shall be evaluated from the perspective of practitioners. The results shall be used to provide a framework that supports project managers of software development projects.

Prerequisites: (1) Basic knowledge of project management for software development projects; (2) Intermediate knowledge of artificial intelligence; (3) Pro-activity, self-organization, attention to detail (desirable).

 

Initial references:

  • Perkusich, Mirko, et al. "Intelligent software engineering in the context of agile software development: A systematic literature review." Information and Software Technology 119 (2020): 106241.
  • Kotti, Z., Galanopoulou, R., & Spinellis, D. (2023). Machine learning for software engineering: A tertiary study. ACM Computing Surveys, 55(12), 1-39.
  • Fridgeirsson, Thordur Vikingur, et al. "An authoritative study on the near future effect of artificial intelligence on project management knowledge areas." Sustainability 13.4 (2021): 2345.

 

Supervisor: Cielo González Moyano

 

 

Topic 6: Literature Review on Business Intelligence and Human Factors (Bachelor or Master in Information Systems)

Motivation & problem: New types of analytical tools fundamentally change the way how process analysts do their work, with the expectation to drastically impact various professional services including auditing or business process management. Recent years have seen an increasing uptake of process mining tools by corporations and by professional services companies, where they are used to support the analysis of business processes. Recent research on the organisational impact of process mining highlights benefits for process awareness and overall value creation, but potential negative effects are hardly understood.

Primary objective: Review the literature on business intelligence and big data analytics and investigate where negative effects as discussed by Sutton et al (2023) and Parasuraman et al (2000) are discussed.

Prerequisites: (1) Basic knowledge of process mining; (2) Basic knowledge of business process management; (3) Interest in human-computer interaction and engineering psychology.

 

Initial References:

  • Sutton, S. G., Arnold, V., & Holt, M. (2023). An extension of the theory of technology dominance: Capturing the underlying causal complexity. International Journal of Accounting Information Systems, 50, 100626.
  • Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of management information systems, 35(2), 388-423.
  • Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans, 30(3), 286-297.
  • Zimmermann, L., Zebra, F., & Weber, B. (2023). What makes life for process mining analysts difficult? A reflection of challenges. Software and Systems Modeling, 1-29.

 

Supervisor: Jan Mendling

 

Topic 7: Visualizing Cyclic Time Arrangements in Process Graphs (Bachelor/Master)

Time is essential to understanding processes, yet most process mining approaches are limited to depicting time within a process graph as textual cues or color schemes. Adapting the visual appearance of process graphs to various time arrangements may enhance the accessibility for finding bottlenecks or delays. An example is aligning process graphs along a linear timeline [1]. In cases where processes involve repetitive patterns, such as in chronic health care or crop management, a cyclic arrangement may be useful. However, for the latter, an adequate solution in process mining is needed.

This thesis aims to develop and exemplify a design method for a visual solution in process mining that allows for exploring a cyclic time arrangement in a process graph. We will adapt the research objectives to align with the experience and study goals of the student.

Initial References:

  • H. Kaur, J. Mendling, C. Rubensson, and T. Kampik, “Timeline-based Process Discovery,” CoRR, abs/2401.04114, 2024. Available: https://doi.org/10.48550/arXiv.2401.04114
  • A. Yeshchenko and J. Mendling, “A Survey of Approaches for Event Sequence Analysis and Visualization using the ESeVis Framework.,” CoRR, abs/2202.07941, 2022. Available: https://arxiv.org/abs/2202.07941
  • W. Aigner, S. Miksch, H. Schumann, and C. Tominski, Visualization of Time-Oriented Data. in Human-Computer Interaction Series. London: Springer London, 2011. Available: https://doi.org/10.1007/978-0-85729-079-3.

 

Supervisor: Christoffer Rubensson

 

Topic 8: Advanced Resource Analysis in Process Mining (Bachelor/Master)

In the last decade, process mining techniques have been developed to study human behavior in event data, such as the strength of collaboration between co-workers or even stress levels at a workplace. Since measuring human behavior is complex, this is a welcoming alternative to more labor-intensive methods like surveys. Still, most techniques are relatively simple but could be improved by applying theoretical frameworks from social science.

This thesis aims to develop a resource analysis approach (e.g., a metric, a concept, or a framework) in process mining grounded in an existing theory from social science. We will adapt the research objectives to align with the experience and study goals of the student.

Initial References:

  • J. Nakatumba and W. M. P. van der Aalst, “Analyzing Resource Behavior Using Process Mining,” in Business Process Management Workshops. BPM 2009. Lecture Notes in Business Information Processing, S. Rinderle-Ma, S. Sadiq, and F. Leymann, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. Available: https://doi.org/10.1007/978-3-642-12186-9_8.
  • A. Pika, M. Leyer, M. T. Wynn, C. J. Fidge, A. H. M. Ter Hofstede, and W. M. P. Van der Aalst, “Mining Resource Profiles from Event Logs,” in ACM Transactions on Management Information Systems, vol. 8, no. 1, 1:1-30, 2017. Available: https://doi.org/10.1145/3041218.
  • Z. Huang, X. Lu, and H. Duan, “Resource behavior measure and application in business process management,” in Expert Systems with Applications, vol. 39, no. 7, 6458–6468, 2012. Available: https://doi.org/10.1016/j.eswa.2011.12.061.

 

Supervisor: Christoffer Rubensson

 

Topic 9: Anthropomorphic Perceptions of Large Language Models: what is the gender of ChatGPT and its Counterparts? (Bachelor/Master)

Description: In today's digital era, Large Language Models (LLMs) like ChatGPT are transforming the way we interact with technology, often blurring the boundaries between machine and human cognition. This thesis delves into the intriguing realm of anthropomorphism, the human tendency to attribute human-like qualities to non-human entities. Specifically, this research aims to uncover laypeople's underlying beliefs and implicit conceptions about ChatGPT and similar models concerning an implicit gender attribution. By designing and conducting a survey, the thesis will gain insights into individuals' perception of these cutting-edge technologies. The findings can potentially illuminate not only our relationship with LLMs but also the broader implications of human-machine interactions in an increasingly AI-driven world.

 

Initial References:

  • Deshpande, A., Rajpurohit, T., Narasimhan, K., & Kalyan, A. (2023). Anthropomorphization of AI: Opportunities and Risks (arXiv:2305.14784). arXiv. https://doi.org/10.48550/arXiv.2305.14784
  • Farina, M., & Lavazza, A. (2023). ChatGPT in society: Emerging issues. Frontiers in Artificial Intelligence, 6. https://www.frontiersin.org/articles/10.3389/frai.2023.1130913
  • Aşkın, G., Saltık, İ., Boz, T. E., & Urgen, B. A. (2023). Gendered Actions with a Genderless Robot: Gender Attribution to Humanoid Robots in Action. International Journal of Social Robotics, 15(11), 1915–1931. https://doi.org/10.1007/s12369-022-00964-0

 

Supervisor: Jennifer Haase

 

Topic 10: Process prediction using object-centric event log (Bachelor/Master)

Business process prediction involves forecasting specific details, such as the next activity to be performed, the time remaining for the completion of a process instance, or key process indicators, for an ongoing process instance. Currently, the techniques rely on XES event logs as input data. However, the field of process mining is shifting towards utilizing object-centric event logs, which offer a comprehensive multidimensional view of the data. Despite this advancement, object-centric event logs have been underutilized as input for process prediction.

Research problem: The core research problem addressed is: How can process prediction benefit from an object-centric event log?
The aim is to propose a method to process prediction using object-centric event log.

Requirements: The candidate must have previous knowledge of process mining and software development. Further desirable requirements are pro-activity and self-organization.

Initial references

  • An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs. ACM Trans. Intell. Syst. Technol. 11(6): 68:1-68:34 (2020)
  • Uncovering Object-Centric Data in Classical Event Logs for the Automated Transformation from XES to OCEL. BPM 2022: 379-396
  • Benedikt Knopp, Wil M. P. van der Aalst:Order Management Object-centric Event Log in OCEL 2.0 Standard. Zenodo, 2023


Supervisor: Kate Revoredo

 

Topic 11: Causation discovery for process prediction (Bachelor/Master)

Business process prediction involves forecasting specific details, such as the next activity to be performed, the time remaining for the completion of a process instance, or key process indicators, for an ongoing process instance. Currently, most techniques rely on the order in which the events happened without considering the cause-effect relation among them.

Research problem: The core research problem addressed is: How can process prediction benefit from the cause-effect relation among the events?
The aim is to propose a method to discover the cause relation among events and use this information for process prediction.

Requirements: The candidate must have previous knowledge of process mining, statistics, and software development. Further desirable requirements are pro-activity and self-organization.

Initial references

  •  An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs. ACM Trans. Intell. Syst. Technol. 11(6): 68:1-68:34 (2020)
  • Jens Brunk, Matthias Stierle, Leon Papke, Kate Revoredo, Martin Matzner, Jörg Becker: Cause vs. effect in context-sensitive prediction of business process instances. Inf. Syst. 95: 101635 (2021)
  • Pearl,J.(2011).Bayesiannetworks.

 

Supervisor: Kate Revoredo

 

Topic 12: Literature review on quality characteristics in dashboards, business intelligence systems, balanced scorecards, and other reporting solutions: a study of visualization methods (Bachelor)

This bachelor thesis aims to conduct a comprehensive literature review on quality characteristics in dashboards, business intelligence systems, balanced scorecards, and other reporting solutions. The focus will be on comparing various visualization methods employed in these systems. The study intends to provide insights into the key features that contribute to the effectiveness and user satisfaction of visual reporting tools, helping to guide the selection and implementation of suitable solutions in diverse organizational contexts.

Initial references:

  • Burstein, F., & Holsapple, C. W. (2008). Handbook on Decision Support Systems 2. https://www.academia.edu/83497312/Handbook_on_Decision_Support_Systems_2
  • Trieu, V.-H. (2023). Towards an understanding of actual business intelligence technology use: An individual user perspective. Information Technology & People, 36(1), 409–432.
  • Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, xiii–xxiii.

 

Supervisor: Kristina Schneider

 

Topic 13: Analysis of theoretical explanations and scientific theories on transitioning from dashboards to decision making in organizational contexts (Bachelor)

This bachelor thesis seeks to analyze theoretical explanations and scientific theories concerning the transition from dashboards to decision-making processes. Dashboards are widely used tools in organizational contexts for decision-making. The study aims to examine the levels of management where dashboards are employed and how they contribute to the decision-making process within organizations.

Initial references:

  • Burstein, F., & Holsapple, C. W. (2008). Handbook on Decision Support Systems 2. https://www.academia.edu/83497312/Handbook_on_Decision_Support_Systems_2
  • Maynard, S., Burstein, F., & Arnott, D. (2001). A multi-faceted decision support system evaluation approach. Journal of Decision Systems, 10(3–4), 395–428.
  • Mintzberg, H., Raisinghani, D., & Theoret, A. (1976). The Structure of “Unstructured” Decision Processes. Administrative Science Quarterly, 21(2), 246. https://doi.org/10.2307/2392045

 

Supervisor: Kristina Schneider

 

Topic 14: Enhancing Student Engagement in Online Learning Environments through Process Mining (Bachelor/Master)

This study investigates how process mining techniques can be leveraged to enhance student engagement within online learning environments. It explores the utilization of data-driven insights to optimize learning pathways, identify patterns of student interaction, and design personalized interventions to foster greater engagement and participation in digital educational platforms.

 

Initial references:

  • Rohani, N., Gal, K., Gallagher, M., Manataki, A. (2023). Discovering Students’ Learning Strategies in a Visual Programming MOOC Through Process Mining Techniques. In: Montali, M., Senderovich, A., Weidlich, M. (eds) Process Mining Workshops. ICPM 2022. Lecture Notes in Business Information Processing, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-27815-0_39
  • Umer, R., Susnjak, T., Mathrani, A. and Suriadi, S. (2017), "On predicting academic performance with process mining in learning analytics", Journal of Research in Innovative Teaching & Learning, Vol. 10 No. 2, pp. 160-176. https://doi.org/10.1108/JRIT-09-2017-0022
  • Nkomo, L.M., Nat, M. Student Engagement Patterns in a Blended Learning Environment: an Educational Data Mining Approach. TechTrends 65, 808–817 (2021). https://doi.org/10.1007/s11528-021-00638-0

 

Supervisor: Rachmadita Andre Swari

 

Topic 15: Runtime Prediction of Alignment Construction Algorithms (Bachelor/Master)

Conformance Checking relates a process model to recorded instances of the execution of the process, typically stored in event logs, to determine where expected and actual behaviour deviate from each other. In this context alignment algorithms are regarded as the de facto standard method, due to their interpretability and accuracy in highlighting precise problem areas in the process. Yet, typically run times for alignment construction are prohibitively large, typically caused by a handful of traces in the log, for which the construction of an alignment is especially complex.
One possible solution to this problem could lie in predicting the expected runtime of aligning single traces in the log, for instance using regression-based methods and then ignoring traces, that are expected to take long.

In this thesis, the student will:

  • derive a methodology for predicting the runtime of alignment construction between event logs and process models
  • evaluate the accuracy of the predictor
  • assess the factors that influence the runtime of alignments

The student is expected to have knowledge of process mining, conformance checking, and basic knowledge of regression analysis, or willingness to dive deep into these topics.

Initial References:

  • Carmona, J., van Dongen, B., Solti, A., & Weidlich, M. (2018). Conformance checking. Switzerland: Springer.
  • Backhaus, K., Erichson, B., Weiber, R., Plinke, W. (2016). Regressionsanalyse. In: Multivariate Analysemethoden. Springer Gabler, Berlin, Heidelberg.


Supervisor: Martin Kabierski

 

Topic 16: Biodiversity-based Saturation for Grounded Theory (Master)

Grounded theory is a research methodology usually applied in qualitative analysis. It involves the collection of data (usually through interviews, surveys, ...), and the deduction of concepts, categories, and ultimately theories that emerge from the collected data. A central question to this iterative data collection-evaluation process is when to stop collecting data. Usually one stops when the categories are saturated, i.e. when no new insights are obtained. Determining when exactly this point has been reached is a topic of discussion and research.
Species richness estimators, that estimate the completeness of samples, could be utilized to give saturation estimates that are data-driven and grounded in statistics.

In this thesis, the student will:

  • assess the applicability of species richness estimation for determining saturation in grounded theory
  • implement and apply the estimator to qualitative interview data
  • evaluate the feasibility of the approach and discuss potential limitations

The student is expected to have a solid understanding of statistics and ideally preliminary experience in the analysis of qualitative data. Note, that the student is not expected to collect data for the thesis. These will be provided by us.


Initial References:

 

  • Strauss, A., & Corbin, J. (1994). Grounded theory methodology: An overview. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 273–285). Sage Publications, Inc.
  • Saunders, Benjamin, et al. (2018). Saturation in qualitative research: exploring its conceptualization and operationalization. In: Qual Quant 52 (pp. 1893-1907). Springer
  • Colwell, Robert K., et al. "Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages." _Journal of plant ecology_ 5.1 (2012): 3-21

 

Supervisor: Martin Kabierski