WU (Vienna University of Economics and Business)

1. Querying Very Large Graph databases

Supervisor: Axel Polleres

2. Visualizing Open Data in Virtual and Augmented Reality - How can AR and VR be used to improve exploration of data?

Supervisor: Johann Mitlöhner

3. Text Mining using Deep Learning with Tensorflow and GPU

Supervisor: Johann Mitlöhner

4. Platform Outages and Online Activity

Supervisor: Johannes Wachs

5. Geographic Dynamics of Software Use

Supervisor: Johannes Wachs

6. Identifying Open Source Software Dependencies in Smartphone Apps

Supervisor: Johannes Wachs

7. The impact of digital technologies on business processes – insights from published case studies

Supervisor: Monika Malinova Mandelburger

Processes deliver value to customers through a repetitive execution of its activities. However, every good process eventually becomes a bad process due to numerous factors such as changing environment (e.g. climate change, pandemic), increased competition and rising customer expectations which influence how a company operates. In order to keep up with all these changes, organizations have to continuously change their business processes. One of the biggest enablers of process change nowadays are the emerging digital technologies.
This thesis should explore the different ways digital technologies such as the Internet of Things affect the business processes of organizations. This should be done by means of a systematic literature review of published scientific papers and/or industry case studies, or by collecting and analysing empirical data on the role of digital technologies on the business processes in organizations.
This thesis topic could be divided into multiple thesis topics, each focusing on a specific digital technology (e.g. AI, VR, AR, Robotics, Automation, Cloud Computing, Data Analytics, etc.).

References:

Mendling, J., Pentland, B. T., & Recker, J. (2020). Building a complementary agenda for business process management and digital innovation. European journal of information systems, 29(3), 208-219.

Bilgeri, D., Gebauer, H., Fleisch, E., & Wortmann, F. (2019). Driving process innovation with IoT field data. MIS Quarterly Executive, 18, 191-207.

Kamalaldin, A., Sjödin, D., Hullova, D., & Parida, V. (2021). Configuring ecosystem strategies for digitally enabled process innovation: A framework for equipment suppliers in the process industries. Technovation, 105, 102250.

Sjödin, D. R., Parida, V., Leksell, M., & Petrovic, A. (2018). Smart Factory Implementation and Process Innovation: A Preliminary Maturity Model for Leveraging Digitalization in Manufacturing Moving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, processes, and technologies. Research-Technology Management, 61(5), 22-31.

Shi, Zhan, et al. "Smart factory in Industry 4.0." Systems Research and Behavioral Science 37.4 (2020): 607-617.

8. How do digital technologies affect the customer’s experience and convenience?

Supervisor: Monika Malinova Mandelburger

9. Executive decision-making with process models

Supervisor: Monika Malinova Mandelburger

10. A Survey of AI Audit Tools

Supervisor: Marta Sabou

Context. Artificial Intelligence (AI) Audits are used to investigate AI systems in many contexts, e.g., security, robustness or fairness. Initial methodologies [1] and tools [2, 3] have been proposed to enable improved AI Auditing processes. The basic audit process consists of the following four steps:

Definition of audit goal and scope: The goal of the scoping stage is to clarify the objective of the audit (e.g. fairness, security, robustness, identifying ML errors,...) of the investigated system.
Preparation of audit: The mapping stage is to identify system components and stakeholders that are relevant to audit goal and scope.
Conduction of audit: The actual “This stage is where the majority of the auditing team’s testing activity is done—when the auditors execute a series of tests to gauge the compliance of the system with the prioritized ethical values of the organization.” [1] Collection of audit trace data is also part of this step.
Postprocessing of audit: This phase of the audit is the more reflective stage, when the results of the tests at the execution stage are analyzed in regard to the expectations raised in Step 1.

Problem. Despite the availability of such methodologies and tools, AI audits are still not yet mature, largely unstructured and time-consuming. Thus, we are interested to understand the capability of existing tools that support/enable auditability of AI systems.

Goal. The thesis would be focusing on conducting a survey on the existing tools (e.g., using the list on [4] as a starting point), to analyze and extract information which kind of AI audits they support and which auditing steps they automate/support. We are mainly interested in Step 2 (Preparation of audits) and parts of Step 3 (Conduction of audits) above.

Expected outcome. The thesis should primarily focus on an analysis of the extracted data (e.g., analyzing/discussing main trends that emerge for the topic).

Thesis type: the topic is suitable both for a Bachelor and a Master thesis.

Initial Reading List.

[1] Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 33–44. doi.org/10.1145/3351095.3372873
[2] I. Naja, M. Markovic, P. Edwards, W. Pang, C. Cottrill and R. Williams, "Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information," in IEEE Access, vol. 10, pp. 74383-74411, 2022, doi: 10.1109/ACCESS.2022.3188967.
[3] Aequitas: A Bias and Fairness Audit Toolkit; arxiv.org/pdf/1811.05577.pdf
[4] List of AI Auditing Initiatives/Tools [public]

11. Benchmarking human-centric ontology defects

Supervisor: Marta Sabou

12. Analyzing Constraint Languages for Knowledge Graphs

Supervisor: Nicolas Ferranti

13. Modeling emerging patterns of Human-AI Collaboration in Hybrid Intelligence Systems

Supervisor: Elmar Kiesling

Background

In recent years, a large stream of AI research has emerged that is not aimed at developing systems that replicate or surpass human intelligence, but rather focused on a
synergistic combination of human and machine intelligence, giving rise to the concept of hybrid intelligence systems [2, 1, 4, 5, 3]. This growing research interest reflects a broader human-centric paradigm that is also evident in industrial policies – such as the European Union Ethics Guidelines for Trustworthy AI, which calls for human agency and oversight. The question how such hybrid intelligent systems and the roles and interactions
between human and artificial agents can be modeled, however, has so far attracted limited attention.

Research Problem

In this Bachelor thesis, you will investigate questions such as

What are appropriate ways to model processes that are performed collaboratively by humans and artificial agents?
How can the process models support the planning and design of hybrid intelligence systems?
How can the process models be used to enact hybrid intelligence workflows?

As a starting point for your investigation, you will collect early reported examples of successful human-machine collaboration (e.g., in business applications such as decision support systems). To this end, you will review the scientific and (optional) gray literature (e.g., publications about funded projects, demonstrators and applications published on portals such as AI4eu etc.) for reported hybrid intelligence applications. Based on the collected examples, you will then identify emerging patterns of human-AI collaboration and investigate to what extent and how these patterns can be modeled using a process modeling language (e.g., imperative languages such as BPMN, declarative languages such as Declare). The thesis will result in a set of modeled examples and modeling insights; these may include successful modeling patterns and/or identified challenges that stem from the tension between the flexible and adaptive nature of hybrid intelligence systems and the constraints imposed by process modeling languages.

References

[1] Zeynep Akata et al. “A research agenda for hybrid intelligence: augmenting human
intellect with collaborative, adaptive, responsible, and explainable artificial intelligence”.
In: Computer 53.08 (2020), pp. 18–28.
[2] Dominik Dellermann et al. “Hybrid intelligence”. In: Business & Information Systems
Engineering 61.5 (2019), pp. 637–643.
[3] Patrick Hemmer et al. “Human-AI Complementarity in Hybrid Intelligence Systems:
A Structured Literature Review.” In: PACIS (2021), p. 78.
[4] Matthew Johnson and Alonso Vera. “No AI is an island: the case for teaming
intelligence”. In: AI magazine 40.1 (2019), pp. 16–28.
[5] Marieke MM Peeters et al. “Hybrid collective intelligence in a human–AI society”.
In: AI & society 36.1 (2021), pp. 217–238.

14. Building Knowledge Graphs from text data

Supervisor: Dawa Chang

Background:

Knowledge graphs (KGs) can be defined as a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent potentially different relations between these entities [1], and it’s especially working well for both humans and machines to consume knowledge. Nowadays KGs are regarded as a key enabler for a number of technologies including artificial intelligence, question answering, personal assistants and more across all sectors and large companies (incl. Microsoft, Google, Facebook, Amazon, Samsung, Ebay and IBM) [2]. Despite of its wide usage and importance, however, KGs are still unfamiliar to many people as a technique to manage data.

Research problem:

The main purpose of this topic is to know what is KGs and how to make it. Also, this topic aims to get familiar with text analysis. In this regard, this thesis aims to (1) see what is KGs and its special characteristics, (2) analyze text data for the purpose of building a RDF Knowledge Graph, (3) try to make a RDF Knowledge Graph with corpora resulted from the text analysis, (4) and figure out what could be needed to build an informative and useful Knowledge Graph. The text data will be provided, but a student is free to choose any other text data or software in discussion with the supervisor of this topic.

Initial references:

Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., ... & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys (CSUR), 54(4), 1-37. https://dl.acm.org/doi/pdf/10.1145/3447772
Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale Knowledge Graphs: Lessons and Challenges: Five diverse technology companies show how it’s done. Queue, 17(2), 48-75. https://dl.acm.org/doi/pdf/10.1145/3329781.3332266
Claudio Gutierrez and Juan F. Sequeda (2019) "A Brief History of Knowledge Graph's Main Ideas: A tutorial" http://knowledgegraph.today/paper.html

15. Deep Learning in Business Process Management: A reproducibility study

Supervisor: Stefan Bachhofner

Deep Learning has had tremendous success in business and science (LeCun, 2015, Schmidhuber, 2015). The paper by (Krizhevsky, 2012) is widely accepted to be the key paper for this success, as they were able to substantially decrease the error on ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC-2012), an image classification task, by using a convolutional neural network. As a consequence, deep learning has been applied to predictive problems in business process management as well – for example (Nguyen, 2020), (Taymouri, 2021), (Park and Song, 2019), (Tax, 2017), (Weinzierl, 2020) and (Obodoekwe, 2022).

The objective of this thesis is to reproduce at least one paper that applies deep learning to a predictive problem in business process management. You can choose one of the ones listed above. The scope will be adapted according to whether it will be a master or bachelor thesis.

Supervisor: Stefan Bachhofner

Resources

STAT 157, Introduction to Deep Learning, UC Berkley, courses.d2l.ai/berkeley-stat-157/index.html

6.S191, Introduction to Deep Learning, Massachusetts Institute of Technology, introtodeeplearning.com

Dive into Deep Learning, d2l.ai

CS231n, Convolutional Neural Networks for Visual Recognition, Stanford University, cs231n.stanford.edu

A collection of lecture on deep learning, Massachusetts Institute of Technology, deeplearning.mit.edu

References

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.

Neu, D. A., Lahann, J., & Fettke, P. (2021). A systematic literature review on state-of-the-art deep learning methods for process prediction. Artificial Intelligence Review, 1-27.

Nguyen, A., Chatterjee, S., Weinzierl, S., Schwinn, L., Matzner, M., & Eskofier, B. (2020, October). Time matters: time-aware LSTMs for predictive business process monitoring. In International Conference on Process Mining (pp. 112-123). Springer, Cham.

Taymouri, F., La Rosa, M., & Erfani, S. M. (2021). A deep adversarial model for suffix and remaining time prediction of event sequences. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) (pp. 522-530). Society for Industrial and Applied Mathematics.

G. Park and M. Song, "Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm," 2019 International Conference on Process Mining (ICPM), 2019, pp. 121-128, doi: 10.1109/ICPM.2019.00027.

Park, G., & Song, M. (2020). Predicting performances in business processes using deep neural networks. Decision Support Systems, 129, 113191.

Tax, N., Verenich, I., La Rosa, M., Dumas, M. (2017). Predictive Business Process Monitoring with LSTM Neural Networks. In: Dubois, E., Pohl, K. (eds) Advanced Information Systems Engineering. CAiSE 2017. Lecture Notes in Computer Science(), vol 10253. Springer, Cham. doi.org/10.1007/978-3-319-59536-8_30

Weinzierl, S., Zilker, S., Brunk, J., Revoredo, K., Matzner, M., & Becker, J. (2020, September). XNAP: making LSTM-based next activity predictions explainable by using LRP. In International Conference on Business Process Management (pp. 129-141). Springer, Cham.

Obodoekwe E, Fang X, Lu K. Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy. Electronics. 2022; 11(14):2128. doi.org/10.3390/electronics11142128

16. Performance Evolution of In-Knowledge Graph Tasks: A Structured Literature Review

Supervisor: Stefan Bachhofner

Knowledge graphs (KG) are means to model a domain of interest via relationships between objects, where the objects are the nodes and the relationships are the edges of a graph (Hogan, 2020). See (Rotmensch, 2017) for an example from medicine. After construction, a KG can be used for downstream tasks, which are grouped into In-KG and Out-of-KG tasks - sometimes also called applications, but we stick with tasks for now - (Wang, 2017). The In-KG tasks are link prediction, triple classification, entity classification, and entity resolution. In the thesis, we are interested in these In-KG tasks and which methods of the past 10 to 12 years have lead to performance gains on these tasks. Within this, we are particularly interested in comparing methods which use knowledge graph embeddings and those which do not. Knowledge graph embeddings are graph embeddings, in other words, the nodes and edges of the knowledge graph are mapped into a continuous vector space – which is referred to as embedding a knowledge graph (Wang, 2017). Embeddings are, however, not limited to nodes and edges but are also done for substructures (subset of nodes and/or edges) and even the whole-graph (Cai, 2018).

The objective of this thesis is to conduct a comprehensive structured literature review (SLR) on the performance evolution of in-KG tasks. Within this (SLR), we are particularly interested in knowledge graph embeddings. We want to understand whether, and to which extend, knowledge graph embeddings lead to performance gains for in-KG tasks. In other words, the comparison between methods that use embeddings and those that do not is of particular interest. In the thesis, the student will systematically characterise the data sets used for in-KG tasks, the methods used for in-KG tasks, and how the performance on in-KG has changed over time, and to which extend embeddings have improved, or not, the performance. The student might also be asked to give her/his opinion on the current state of the art. The scope will be adapted according to whether it will be a master or bachelor thesis.

Supervisor: Stefan Bachhofner

Resources

CS 520, Knowledge Graphs, Stanford University, https://web.stanford.edu/class/cs520/, web.stanford.edu/~vinayc/kg/notes/Table_Of_Contents.html

References

Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., ... & Zimmermann, A. (2020). Knowledge graphs. arXiv preprint arXiv:2003.02320.

Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., & Sontag, D. (2017). Learning a health knowledge graph from electronic medical records. Scientific reports, 7(1), 1-11.

Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724-2743.

Cai, H., Zheng, V. W., & Chang, K. C. C. (2018). A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering, 30(9), 1616-1637.

17. Performance Evolution of Out-of-Knowledge Graph Tasks: A Structured Literature Review

Supervisor: Stefan Bachhofner

Knowledge graphs (KG) are means to model a domain of interest via relationships between objects, where the objects are the nodes and the relationships are the edges of a graph (Hogan, 2020). See (Rotmensch, 2017) for an example from medicine. After construction, a KG can be used for downstream tasks, which are grouped into In-KG and Out-of-KG tasks - sometimes also called applications, but we stick with tasks for now - (Wang, 2017). The Out-of-KG tasks are relation extraction, question answering, and recommender systems. In the thesis, we are interested in these Out-of-KG tasks and which methods of the past 10 to 12 years have lead to performance gains on these tasks. Within this, we are particularly interested in comparing methods which use knowledge graph embeddings and those which do not. Knowledge graph embeddings are graph embeddings, in other words, the nodes and edges of the knowledge graph are be mapped into a continuous vector space – which is referred to as embedding a knowledge graph (Wang, 2017). Embeddings are, however, not limited to nodes and edges but are also done for substructures (subset of nodes and/or edges) and even the whole-graph (Cai, 2018).

The objective of this thesis is to conduct a comprehensive structured literature review (SLR) on the performance evolution of Out-of-KG tasks. Within this (SLR), we are particularly interested in knowledge graph embeddings. We want to understand whether, and to which extend, knowledge graph embeddings lead to performance gains for Out-of-KG tasks. In other words, the comparison between methods that use embeddings and those that don’t is of particular interest. In the thesis, the student will systematically characterise the data sets used for Out-of-KG tasks, the methods used for Out-of-KG tasks, and how the performance of Out-of-KG tasks has changed over time, and to which extend embeddings have improved, or not, the performance. The student might also be asked to give her/his opinion on the current state of the art. The scope will be adapted according to whether it will be a master or bachelor thesis.

Supervisor: Stefan Bachhofner

Resources

CS 520, Knowledge Graphs, Stanford University, https://web.stanford.edu/class/cs520/, web.stanford.edu/~vinayc/kg/notes/Table_Of_Contents.html

References

Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., ... & Zimmermann, A. (2020). Knowledge graphs. arXiv preprint arXiv:2003.02320.

Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., & Sontag, D. (2017). Learning a health knowledge graph from electronic medical records. Scientific reports, 7(1), 1-11.

Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724-2743.

Cai, H., Zheng, V. W., & Chang, K. C. C. (2018). A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering, 30(9), 1616-1637.

18. Examining Cognitive Effectiveness of Process Mining Representations

Supervisor: Djordje Djurica

19. Measuring the availability of Open Datasets in the web, a consolidation work in monitoring Open Data portals

Supervisors: Shahrom H. Sohi, Axel Polleres

Background

Open Data has increased in popularity and many private and public stakeholders want to promote transparency and enable new business models: this access is provided by different means. The purpose is to provide direct machine readable access to the information and foster democracy and innovative reuse of publicly available data [1].

This movement has already many years, therefore the organisations have already an history on how to treat its access. The work from Neumair and Polleres, 2018 [2], analysed the Open Data Portals measuring its quality and proposing metrics, assessing “the goodness” of these Open Data sources. This represents a “generic formal model to represent data and metadata in web portals”[2]. This project has been implemented in Portal Watch data.wu.ac.at/portalwatch.

This project has been interrupted after the work of Thomas Weber in 2020 aic.ai.wu.ac.at/~polleres/supervised_theses/Thomas_Weber_BSc_2020.pdf [3]. It requires to be monitored again. The research aims to continue and extend the work analysing the new sources of data and integrate into a new version of dashboard in the data.wu.ac.at website.

Research problem

In this Bachelor thesis, you will investigate questions such as:

RQ1: What are the open data sources that are present on these archives? What is the best way to visualise the quality of data and its availability?

RQ2: What is the most continuous source of open data and why?

The goal is to consolidate the previous work and to provide ways to communicate open data portals to different stakeholders. This project suits people who are interested and willing to deepen knowledge in data analysis and data management techniques: foster their knowledge of SQL/Python, visualisation techniques.

Meet the supervisor

Feel free to book a meeting if you’re willing to discuss the topic use its Calendly
calendly.com/shahrom-sohi/30min

Reference

[1] Gurstein, 2011 “Open data: Empowering the empowered or effective data use for everyone?”

firstmonday.org/article/view/3316/2764

[2] Neumair and Polleres, 2018 “Enabling Spatio-Temporal Search in Open Data”

research.wu.ac.at/en/publications/enabling-spatio-temporal-search-in-open-data-15

[3] Weber, 2020 “Open Dataset Archive”, Bachelor Thesis

aic.ai.wu.ac.at/~polleres/supervised_theses/Thomas_Weber_BSc_2020.pdf

20. Accessing open transport information for service oriented mobility

Supervisor: Shahrom H. Sohi

Background

Raising the population in urban areas challenges transportation practitioners to design more efficient mobility solutions. At the same time new forms of mobility are emerging accessible digitally [1]. Transport mode choice analysis takes care of the core factors that affect a user when they are planning to move such as Comfort, Time and Cost [2]. The growth of access to transport via digital interfaces is representing a transport need, these information need to take into account factors affecting usage of mode of transport.

At the same time Open Data increased in popularity and many private and public stakeholders want to promote transparency and enable new business models: this access is provided by different means. The purpose is to provide direct machine readable access to the information and foster democracy and innovative reuse of publicly available data [3]. Accessibility such as of the information can be explained with modern data quality assessments [4]

Research problem

In this Bachelor thesis, you will investigate questions such as:

RQ: What are the transport planning related information accessible as open data?

RQ: Can this information be categorised according to factors affecting mode of transport choice and how?

RQ: What are the benefits for transport stakeholders?

At the beginning you navigate into the basics of the mobility domain: Why do people/goods move in this way? After gathering the essential information about Transport Planning and understanding what can be useful for transport info. You collect information about availability datasets and analyse them. Additionally you can propose a new/ use case of your choice in the mobility domain selecting one of the Austrian Mobility providers or choose one past example. The thesis will be ideal for people who are curious about the mobility world and would like to dig into Data Management Specialisations. You will work with Shahrom Sohi, who is looking for transportation challenges that combine data technologies and international best practices.

Meet the supervisor

Feel free to book a meeting if you’re willing to discuss the topic use its Calendly
calendly.com/shahrom-sohi/30min

Reference

[1] Shaheen et al 2020 “Mobility on Demand Planning and Implementation: Current Practices, Innovations, and Emerging Mobility Futures”

URL : rosap.ntl.bts.gov/view/dot/50553

[2] Geurs and van Wee 2004 “Accessibility evaluation of land-use and transport strategies: review and research directions“

www.sciencedirect.com/science/article/pii/S0966692303000607

[3] Gurstein, 2011 “Open data: Empowering the empowered or effective data use for everyone?”

firstmonday.org/article/view/3316/2764

[4] Neumair and Polleres, 2018 “Enabling Spatio-Temporal Search in Open Data”

research.wu.ac.at/en/publications/enabling-spatio-temporal-search-in-open-data-15

21. Analysing the evolution of community-driven (sub-)schemas within Wikidata

Supervisors: Axel Polleres and Nicolas Ferranti

22. The self concept in Spiritual Knowledge Management

Supervisors: Alexander Kaiser, Birgit Fordinal

The field of knowledge management (KM) has undergone a fundamental transformation, and currently new ideas to re-specify the future role of KM research and practice in a changing and increasingly dynamic world are reflected. One such new idea is the concept of Spiritual Knowledge Management. The core process of Spiritual Knowledge Management is to enable, manage and organize the deep learning process in order to develop the best version of oneself (individual) or itself (organization). One fundamental dimension and key element of spirituality, which can be found in almost all definitions and approaches of spirituality - even if they are sometimes quite different - is the self. Spirituality is connected inseparably with a continuous evolution of the self towards a fully developed and fully unfolded self.

This understanding of spirituality is in line with a number of authors - from very different fields and backgrounds - who all distinguish between different forms of the self over time.

RichardBoyatzis, who comes from the field of organizational behaviour and coaching, differentiates between the real self, that is the self a person currently is and the ideal self, this is the self a person could be [Boyatzis and Akrivou, 2006], [Boyatzis and Dhar, 2021]. Otto Scharmer, whose background is the leadership and change management area as well as the action research field, distinguishes between one’s current “self” and the emerging future “Self” that represents one’s greatest potential [Scharmer and Kaufer, 2013]. Richard Rohr comes from Catholic spirituality and differentiates between the true self and the false self [Rohr, 2013]. He argues that the true self is that part of a person that knows who you are, and whose you are. Matthew Kelly, whose background is consulting as well as spirituality proposes the concept of a ’best-version-of-myself’ in comparison to the current self [Kelly, 2004] and suggests that by finding legitimate needs, deepest desires and talents, one may be able to find this ‘best-version-of-myself’ [Kelly, 2017]. From a completely different background comes knowledge management theorist Ikujiro Nonaka. He describes knowledge creation as a continuous selftranscendental process through which one transcends the boundary of the old self (individually or as an organization) into a new self by acquiring and creating a new context, a new world view and novel knowledge [Nonaka and Konno, 1998]. Also the psychiatrist and psychotherapist Viktor E. Frankl focuses on the self aspect. He was the first to propose and introduce the term self-transcendence in detail and argued that human existence is always directed to something, or someone, other than itself and termed this constitutive characteristic of human existence ”self-transcendence” [Frankl, 1966]. Stam et al. [Stam et al., 2014] argue that the self-concept is how we perceive and what we know about ourselves, respectively. At the same time, they differentiate between the self as we currently experience it and the possible self.

In this bachelor thesis, the different self concepts and the different notions of best version of self, ideal self, true self, self, etc. will not only be outlined and described, but compared with each other. The main interest here is in the underlying assumptions and concepts of the different self concepts and how they fit together and/or also differ fundamentally.

This bachelor thesis can only be assigned to students who are very interdisciplinary oriented and interdisciplinary interested and who are highly open minded.

Ein Vorabgespräch mit einem der beiden Betreuer ist zwingend erforderlich!

Supervisor: Alexander Kaiser and Birgit Fordinal

Literatur:

[Boyatzis and Dhar, 2021] Boyatzis, R. and Dhar, U. (2021). Dynamics of the ideal self. Journal of Management Development, 41(1):1–9.

[Boyatzis and Akrivou, 2006] Boyatzis, R. E. and Akrivou, K. (2006). The ideal self as the driver of intentional change. Journal of management development.

[Bratianu, 2015] Bratianu, C. (2015). Spiritual knowledge. Organizational knowledge dynamics: Managing knowledge creation, acquisition, sharing, and transformation, pages 72–102.

[Bratianu, 2017] Bratianu, C. (2017). Emotional and spiritual knowledge. In Knowledge and Project Management, pages 69–91. Springer.

[Frankl, 1966] Frankl, V. E. (1966). Self-transcendence as a human phenomenon. Journal of Humanistic Psychology, 6(2):97–106.

[Grisold and Kaiser, 2017] Grisold, T. and Kaiser, A. (2017). Leaving behind what we are not: Applying a systems thinking perspective to present unlearning as an enabler for finding the best version of the self. Journal of Organisational Transformation & Social Change, 14(1):39–55.

[Kelly, 2004] Kelly, M. (2004). The rhythm of life: Living every day with passion and purpose. Simon and Schuster.

[Kelly, 2017] Kelly, M. (2017). Perfectly Yourself: Discovering God’s Dream for You. Beacon Publishing.

[Nonaka and Toyama, 2007] Nonaka, I. and Toyama, R. (2007). Strategic management as distributed practical wisdom (phronesis). Industrial and corporate change, 16(3):371–394.

[Rocha and Pinheiro, 2019] Rocha, R. and Pinheiro, P. (2019). Spirituality in knowledge management: Systematic literature review and future studies suggestions. In European Conference on Knowledge Managemen, volume 1, pages 892–XXVI. Academic Conferences and Publishing International

[Rohr, 2011] Rohr, R. (2011). Falling upward: A spirituality for the two halves of life. John Wiley & Sons.

[Rohr, 2013] Rohr, R. (2013). Immortal diamond: The search for our true self. John Wiley & Sons.

[Scharmer, 2001] Scharmer, C. (2001). Self-transcending knowledge. sensing and organizing around emerging opportunities. Journal of Knowledge Management, 5(2):137–150.

[Scharmer, 2009] Scharmer, C. O. (2009). Theory U: Learning from the future as it emerges. Berrett-Koehler Publishers.

[Stam et al., 2014] Stam, D., Lord, R. G., Knippenberg, D. v., and Wisse, B. (2014). An image of who we might become: Vision communication, possible selves, and vision pursuit. Organization Science, 25(4):1172–1194.

[Wong, 2016] Wong, P. T. (2016). Meaning-seeking, self-transcendence, and well-being. In Logotherapy and existential analysis, pages 311–321. Springer.

23. Exploring the Relationship between Practical Wisdom and Performance: Employing the Organizational Phronesis Scale

Supervisor: Florian Kragulj

Name	Purpose	Lifetime	Provider
CookieConsent	Saves your consent to using cookies.	30 days	WU
site-popup	Saves if popup was filled or closed.	30 days	WU
BACH_PRXY_ID	To be able to display some WU-specific content, it is necessary that some information must be accessed by back-end WU systems. Required to assign the appropriate answer to a request.	20 years	WU
BACH_PRXY_SN	To be able to display some WU-specific content, it is necessary that some information must be accessed by back-end WU systems. Required to assign the appropriate answer to a request.	session	WU
fe_typo_user	Required for login and access to protected content or for editing the user’s personal profile.	session	WU
be_typo_user	Required for login and editing content in the TYPO3 back end.	session	WU
be_lastLoginProvider	Stores the last method used for logging in to the TYPO3 back end.	90 days	WU
ASP.NET_SessionId	Required for assigning visitors to forms.	session	WU (forms.wu.ac.at)
__RequestVerificationToken	Required to protect forms against attacks.	session	WU (forms.wu.ac.at)
ESRASOFTSID	Required for identifying the logged-in user in the Business Language Center’s course registration system.	session	WU (esrasoft.wu.ac.at)
esraSoftWiData	Required to track the language and language courses selected by the user.	session	WU (esrasoft.wu.ac.at)
esraSimpleSAMLAuthToken	Required for identifying WU employees during the course registration process.	session	WU (esrasoft.wu.ac.at)
esraSimpleSAML	Required for identifying WU employees during the course registration process.	session	WU (esrasoft.wu.ac.at)
SimpleSAML	Required for identifying WU employees during the course registration process.	session	WU (esrasoft.wu.ac.at)

Name	Purpose	Lifetime	Provider
_pk_id	Used by Matomo Analytics to store a few details about the user, such as the unique visitor ID.	30 days	WU (piwik.wu.ac.at)
_pk_ref	Used by Matomo Analytics to store the attribution information, the referrer initially used to visit the website.	6 months	WU (piwik.wu.ac.at)
_pk_ses	Created by Matomo Analytics, short-lived cookies used to temporarily store data for the current visit.	1 hours	WU (piwik.wu.ac.at)
_gcl_au	Contains a randomly generated user ID.	3 months	Google
AMP_TOKEN	Contains a token that can be used to retrieve a Client ID from AMP Client ID service. Other possible values indicate opt-out, inflight request or an error retrieving a Client ID from AMP Client ID service.	1 year	Google
_dc_gtm_--property-id--	Used by DoubleClick (Google Tag Manager) to help identify the visitors by either age, gender or interests.	2 years	Google
_ga	Contains a randomly generated user ID. Using this ID, Google Analytics can recognize returning users on this website and merge the data from previous visits.	2 year	Google
_gat_gtag	Certain data is only sent to Google Analytics a maximum of once per minute. As long as it is set, certain data transfers are prevented.	1 minute	Google
_gid	Contains a randomly generated user ID. Using this ID, Google Analytics can recognize returning users on this website and merge the data from previous visits.	24 hour	Google
_gac_gb	Contains campaign-related information for the user. If Google Analytics and Google Ads accounts are linked, the conversion tags on the Google Ads website read this cookie.	90 day	Google
_dc_gtm	Used to throttle the request rate.	1 minute	Google
IDE	Contains a randomly generated user ID. Using this ID, Google can recognize the user across different websites across domains and display personalized advertising.	1 year	Google
player	This cookie saves user-specific settings before an embedded Vimeo video is played. This means that the next time you watch a Vimeo video, your preferred settings will be loaded.	1 year	Vimeo
vuid	This cookie is used to save the usage history of the user.	2 year	Vimeo
__cf_bm	This cookie is used to distinguish between humans and bots. This is necessary for Vimeo to collect valid data about the use of the service.	1 day	Vimeo
_uetvid	This cookie is set to enable the use of the Vimeo video player.	1 year	Vimeo
_tt_enable_cookie	This cookie is used to enable the vimeo video embedding on the WU Website and for other unspecified purposes.	1 year	Vimeo
afUserId	This cookie collects data from users who interact with embedded Vimeo videos.	2 years	Vimeo
_abexps	This cookie saves settings made by the user, e.g. Default language, region or username as well as interaction data of the user with Vimeo	10 months	Vimeo
_clck	This cookie enables the use of the embedded Vimeo video player	1 year	Vimeo
has_logged_in	This cookie stores login information and if the user has ever logged in.	10 years	Vimeo
language	This cookie remembers the language setting of a user. This ensures that Vimeo appears in the language selected by the user.	11 years	Vimeo
_ttp	This cookie is set to enable the use of the Vimeo video player	1 year	Vimeo
sd_client_id	This cookie stores data about the users current video settings and a personal identification token	2 year	Vimeo
_rdt_uuid	This cookie collects data about the users actions on websites that have a vimeo video embedded.	3 months	Vimeo
vimeo_cart	This cookie is used to check how many times a video has been played by the user.	10 years	Vimeo
OptanonConsent	This cookie stores information about the consent status of a visitor.	1 year	Vimeo
_scid	This cookie is used to assign a unique ID to a user	10 months	Vimeo

Name	Purpose	Lifetime	Provider
test_cookie	Is set as a test to check whether the browser allows cookies to be set. Does not contain any identification features.	15 minute	Google
IDE	Contains a randomly generated user ID. Using this ID, Google can recognize the user across different websites across domains and display personalized advertising.	1 year	Google
_gcl_au	Contains a randomly generated user ID.	90 day	Google
_gcl_aw	This cookie is set when a user clicks on a Google ad on the website. It contains information about which ad was clicked.	90 day	Google
xs	Used to maintain a Facebook session. It works in combination with the c_user cookie to authenticate the user's identity on Facebook.	1 year	Facebook
fr	Used to serve advertisements and measure and improve their relevance.	90 day	Facebook
m_pixel_ratio	Performance cookie used by Facebook with Facebook pixels.	session	Facebook
wd	Used for analysis purposes. Technical parameters are logged (e.g. aspect ratio and dimensions of the screen) so that Facebook apps can be displayed correctly.	7 day	Facebook
dpr	Used for analysis purposes. Technical parameters are logged (e.g. aspect ratio and dimensions of the screen) so that Facebook apps can be displayed correctly.	7 day	Facebook
sb	Used to save browser details and Facebook account security information.	2 year	Facebook
dbln	Used to save browser details and Facebook account security information.	2 year	Facebook
spin	Cookie for advertising purposes and reporting on social campaigns.	session	Facebook
presence	Contains the "Chat" status of a logged in user.	1 month	Facebook
x-referer	Performance cookie that is used by Facebook in combination with Facebook pixels.	session	Facebook
cppo	Cookie for statistical purposes.	90 day	Facebook
datr	Identifies the browser for security and website integrity purposes, including account recovery and identification of potentially compromised accounts.	2 year	Facebook
locale	Saves language settings.	session	Facebook
_fbp	A cookie for Facebook advertising that is used to track and improve relevance and to serve ads on Facebook.	90 day	Facebook
_fbc	A cookie for Facebook advertising that is used to track and improve relevance and to serve ads on Facebook.	90 day	Facebook
UserMatchHistory	This cookie is used to synchronize the LinkedIn Ads IDs.	30 day	LinkedIn
AnalyticsSyncHistory	This cookie saves the time at which the user was synchronized with the "lms_analytics" cookie.	30 day	LinkedIn
li_oatml	This cookie is used to identify LinkedIn members outside of LinkedIn for advertising and analysis purposes.	30 day	LinkedIn
lms_ads	This cookie is used to identify LinkedIn members outside of LinkedIn.	30 day	LinkedIn
lms_analytics	This cookie is used to identify LinkedIn members for analysis purposes.	30 day	LinkedIn
li_fat_id	This cookie is an indirect member identification that is used for conversion tracking, retargeting and analysis.	30 day	LinkedIn
li_sugr	This cookie is used to determine probabilistic matches of the identity of a user.	90 day	LinkedIn
U	This cookie identifies the user’s browser.	3 month	LinkedIn
_guid	This cookie is used to identify a LinkedIn member for advertising via Google Ads.	90 day	LinkedIn
BizographicsOptOut	This cookie is used to determine the rejection status for tracking by third-party providers.	10 year	LinkedIn
lidc	This cookie makes it easier to select LinkedIn's data center.	24 hours	LinkedIn
aam_uuid	This cookie is used for ID synchronization with Adobe Audience Manager.	30 days	LinkedIn
AMCV_XXX_at_AdobeOrg	This cookie contains a unique identifier for the Adobe Experience Cloud.	180 days	LinkedIn
li_mc	This cookie is used as a temporary cache. It is used to have the user's consent information from the database available client side.	2 years	LinkedIn
lang	This cookie stores the language settings of a user. This ensures that the LinkedIn.com website appears in the language selected by the user.	session	LinkedIn
twll	This cookie is set when Twitter is embedded on the page. Twitter collects data that is mainly used for tracking and targeting.	4 year	Twitter
secure_session	This cookie is set when Twitter is embedded on the page. E.g. Twitter's like or sharing functions.	14 year	Twitter
guest_id	This cookie is set by Twitter when a visitor shares content from the WU website on Twitter.	2 year	Twitter
personalization_id	This cookie is set by Twitter to measure the performance of Twitter advertising campaigns in a user's browsers and devices	2 year	Twitter
remember_checked	This cookie is set by when Twitter is embedded on the page. Twitter collects data that is mainly used for tracking and targeting.	4 year	Twitter
remember_checked_on	This cookie is set when Twitter is embedded on the page. E.g. Twitter's like or sharing functions.	4 year	Twitter
mbox	This cookie is intended for identifying Twitter users, for analyzing interaction with the Twitter Service and advertising whitin the service.	2 years	Twitter
guest_id_ads	This cookie is set due to Twitter integration and for sharing content to social media.	10 months	Twitter
d_prefs	This cookie ist used to check referral links and the login status.	90 days	Twitter
ct0	This cookie is set due to Twitter integration and sharing capabilities for the social media.	10 months	Twitter
kdt	This cookie is used to monitor the users login status on Twitter.	10 months	Twitter
guest_id_marketing	This cookie is set for tracking and analytics purposes.	10 months	Twitter
twid	This cookie checks if you are logged in to Twitter during a browser session.	1 year	Twitter
auth_token	This cookie is required for authentication and checks whether the user is logged in.	10 months	Twitter
external_referer	This cookie collects statistical data, including how often you visit Twitter and how long a user stays on Twitter.	1 day	Twitter
NID	This cookie contains a unique ID that is used to save user-specific settings and other information, in particular your preferred language, how many search results should be displayed per page and whether the Google SafeSearch filter should be activated.	6 month	YouTube
1P_JAR	This Google cookie is used to optimize advertising, to provide ads that are relevant to users, to improve reports on campaign performance or to prevent a user from seeing the same ads multiple times.	1 month	YouTube
CONSENT	This cookie is used to support Google's advertising services.	20 year	YouTube
OTZ	Aggregated analysis of website visitors.	17 day	YouTube

Current Thesis Topics

WS 2022/2023

1. Querying Very Large Graph databases

2. Visualizing Open Data in Virtual and Augmented Reality - How can AR and VR be used to improve exploration of data?

3. Text Mining using Deep Learning with Tensorflow and GPU

4. Platform Outages and Online Activity

5. Geographic Dynamics of Software Use

References:

6. Identifying Open Source Software Dependencies in Smartphone Apps

References:

7. The impact of digital technologies on business processes – insights from published case studies

8. How do digital technologies affect the customer’s experience and convenience?

9. Executive decision-making with process models

10. A Survey of AI Audit Tools

11. Benchmarking human-centric ontology defects

12. Analyzing Constraint Languages for Knowledge Graphs

Background

Goal of the thesis

Requirements

Initial references

13. Modeling emerging patterns of Human-AI Collaboration in Hybrid Intelligence Systems

Background

Research Problem

References

14. Building Knowledge Graphs from text data

15. Deep Learning in Business Process Management: A reproducibility study

16. Performance Evolution of In-Knowledge Graph Tasks: A Structured Literature Review

17. Performance Evolution of Out-of-Knowledge Graph Tasks: A Structured Literature Review

18. Examining Cognitive Effectiveness of Process Mining Representations

19. Measuring the availability of Open Datasets in the web, a consolidation work in monitoring Open Data portals

Background

Research problem

Meet the supervisor

Reference

20. Accessing open transport information for service oriented mobility

Background

Research problem

Meet the supervisor

Reference

21. Analysing the evolution of community-driven (sub-)schemas within Wikidata

22. The self concept in Spiritual Knowledge Management

23. Exploring the Relationship between Practical Wisdom and Performance: Employing the Organizational Phronesis Scale