Current Thesis Topics

Background: Organizations cannot be viewed in isolation. Moreover, they are embedded inside a complex ecosystem of competitors, suppliers, customers, governments, and many more. Over time, organizations inside this ecosystem are subject to certain mimetic pressures that lead to unintentional or intentional changes, which in turn can lead to reciprocal imitation – Especially in dynamic and emerging areas like the process mining industry. Mimetic pressures lead to the prevalence of certain practices that seemingly caused success within the focal organization’s industry because those organizations follow the same goals, resources, customers, human capital or experience similar constraints. The new institutional theory provides an important explanation for inter‐organizational imitation. In response to the question raised by Hannan and Freeman in their seminal paper on organizational ecology (1977), ‘Why are there so many kinds of organizations’, DiMaggio and Powell (1983, p. 148) asked: ‘Why is there such startling homogeneity of organizational practices?’

Research Problem:

Currently, the process mining landscape is characterized by a relatively homogenous ecosystem with many organizations that implemented a similar technological foundation, serve the same group of customers, and select the same kind of strategical partnerships. Up to this point, the dynamics behind this development in the process mining industry are completely unknown. The goal of this master thesis would be to pick up the question raised by DiMaggio and Powell by conducting foundational work in the area of process mining. This could, for example, take the form of a case study of a process mining provider, accompanied by an intensive analysis of its history inside the ecosystem and its development over time.

Three initial references:

To Conform or To Perform? Mimetic Behaviour,

Legitimacy-Based Groups and Performance

Consequences

DiMaggio and Powell. 1983. The Iron Cage Revisited: Institutional Isomorphism and collective rationality in Organizational fields. American Sociological Review 48 pp. 147-160.

Haveman. 1993. Follow the Leader: Mimetic Isomorphism and Entry into New Markets. Administrative Science Quarterly (38:4) pp. 593-627.

Hefu Liu et al. 2010. To Conform or To Perform? Mimetic Behaviour, Legitimacy-Based Groups and Performance Consequences. Journal of Operations Management 28(5) pp. 372-384.

H. H. Teo, K. K. Wei and I. Benbasat. 2003. Predicting Intention to Adopt Interorganizational Linkages: An Institutional Perspective. MIS Quarterly 27(1) pp. 19-49.

Hannan, M. T. and Freeman, J. 1977. The population ecology of organizations. American Journal of Sociology 82 pp. 929–64.

Ansari, Garud & Kumaraswamy. 2015. The disruptor's dilemma: TiVo and the U.S. television ecosystem. Strategic Management Journal 37(9) pp. 1829-1853.

Visual analytics is a research area that has developed various techniques to analyze complex data. A particular area of visual analytics focuses on event sequence data, data that is also used in process mining where it is called event logs.

The research problem of this thesis is to investigate which techniques of visual analytics are particularly suited for process mining and business process analysis. To this end, various proposals described in the literature should be analyzed and tried out on data of the recent BPI Challenges.

References:

Yi Guo, Shunan Guo, Zhuochen Jin, Smiti Kaul, David Gotz, Nan Cao: Survey on Visual Analysis of Event Sequence Data, https://arxiv.org/abs/2006.14291.

Aigner, W., Miksch, S., Schumann, H., & Tominski, C. (2011). Visualization of time-oriented data. Springer Science & Business Media.

Yeshchenko, A., Di Ciccio, C., Mendling, J., & Polyvyanyy, A. (2019, November). Comprehensive process drift detection with visual analytics. In International Conference on Conceptual Modeling (pp. 119-135). Springer, Cham.

Type of Thesis: Master Thesis

Business Process Innovation, Business Model Innovation, and Product Innovation are arguably interconnected. However, research usually investigates each of these components of innovation individually. A comprehensive picture how Business Process Innovation, Business Model Innovation, and Product Innovation are connected is missing.
In this thesis, you will use bibliographic analysis in order to provide an overview of Business Process Innovation, Business Model Innovation, and Product Innovation and how they are interconnected.

For further questions, please contact Bastian Wurm (bastian.wurm@wu.ac.at).

Starting Literature:
Groß, S., Malinova, M., & Mendling, J. (2019, January). Navigating through the maze of business process change methods. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. scientometrics, 84(2), 523-538.
Chesbrough, H.(2010). Business model innovation: opportunities and barriers. Long range planning, 43(2-3), 354-363.

There are various languages that support the executable specification of business processes including BPEL, AWS Step Functions or recently Factscript. Programs are written as computer code in these languages.

The research problem of this thesis is to implement a transformation from the Factscript language to a visual representation. Key challenges in this regard will be the automatic layout of the resulting process model.

References

Maxim Vidgof, Philipp Waibel, Jan Mendling, Martin Schimak, Alexander Seik and Peter Queteschiner: A Code-efficient Process Scripting Language, ER Conference 2020.

Purchase, H. C. (2000). Effective information visualisation: a study of graph drawing aesthetics and algorithms. Interacting with computers, 13(2), 147-162.

Gschwind, T., Pinggera, J., Zugal, S., Reijers, H. A., & Weber, B. (2014). A linear time layout algorithm for business process models. Journal of Visual Languages & Computing, 25(2), 117-132.

Writing smart contract code is difficult due to the various specifics of blockchain technology. For this reason, several approaches have been proposed to generate smart contract code from more user-friendly languages.

The research problem of this thesis is to implement a transformation from the Factscript language to Ethereum. To this end, the various constructs of the Factscript have to be studied and mapped to corresponding Ethereum Solidity primitives.

References

Weber, I., Xu, X., Riveret, R., Governatori, G., Ponomarev, A., & Mendling, J. (2016, September). Untrusted business process monitoring and execution using blockchain. In International Conference on Business Process Management (pp. 329-347). Springer, Cham.

Maxim Vidgof, Philipp Waibel, Jan Mendling, Martin Schimak, Alexander Seik and Peter Queteschiner: A Process Scripting and Execution Environment, ER Proceedings 2020.

López‐Pintado, O., García‐Bañuelos, L., Dumas, M., Weber, I., & Ponomarev, A. (2019). Caterpillar: a business process execution engine on the Ethereum blockchain. Software: Practice and Experience, 49(7), 1162-1193.

Xu, X., Weber, I., & Staples, M. (2019). Architecture for blockchain applications (pp. 1-307). Heidelberg: Springer.

There is increasing pressure for more transparency, easier access and openness in scientific research, as shown by initiatives such as the Go FAIR [7] Initiative by 

the research data alliance (RDA), https://www.go-fair.org/, or the increasing demand for more open access to research publications:  

While traditionally research publications have been often published by commercially acting publishers, the EU and many other funding agencies now typically demand

Open Access to all research artefacts and publications they fund, as far a this is not possible and not precluded for instance by sensitivity or legitimate business 

interests (for instance regarding patents, personal data, etc.).

In order to fullfill the requirements for Open Access, there are different models [2]: Publishing with non-commercial publishers and journals who make their publications freely available automatically (Diamond or Platinum Open Access), or respectively paying a fee to publishers as an author (Gold Open Access). Alternatively, many publishers, while charging for their publications, at least permit authors to make their own articles available on the authors personal Webpage or an institutional electronic publications repository (Green Open Access). Still, many authors do not fully make use of the latter option, 

The goal of this thesis is a data science project to 

 a) assess the potential of Green Open Access at WU, by analysing different sources such as FIDES [3] and ePub [4] in order to assess to what extend WU's authors use Green Open Access opportunities.

 b) develop a tool that flags such Open access options to authors

The main difficulties/challenges in this task will be data integration (ambiguous or misspelt author names, publication titles or  journal names) as well as buildign up a knowledge base for Green Open Access options. The latter at least is already available as a tool for WU researchers through the SHERPA ROMEO database [5].

The topic could be split up into several topics or being worked on collaboratively by more than one student, focusing on complementary aspects, such as:

* data integration/entity resolution

* making consolidated metadata available as Linked Open Data  [6] according to the FAIR principles [7]

* developing a tool usable for WU's authors or WU's library to flag Green Open Access opportunities.

2. https://en.wikipedia.org/wiki/Open_access

3. https://bach.wu.ac.at/d/research/

4. https://epub.wu.ac.at/

5. https://v2.sherpa.ac.uk/romeo/

6. https://www.w3.org/egov/wiki/Linked_Open_Data

7. https://www.nature.com/articles/sdata201618

Das folgende ist ein Literatur-Thema, bzw. ein Survey über bestehende Systeme im Bereich Forschungsdatenmanagement.

Description: 

There is increasing pressure for more transparency, easier access and openness in scientific research, as shown by initiatives such as the Go FAIR [1] Initiative by 
the research data alliance (RDA), https://www.go-fair.org/, or the increasing demand for more open access to research publications and more effective management 

of data assets connected to research results.

In this thesis, the goal is to survey the landscape of available research data management platforms and compare them along a set of to be defined criteria.

The objective is to answer the following research question

       "Which systems and platforms could a university like WU use to manage their research data?"

by analysing and categorising the landscape of available tools and  platforms in this area, find out  about their adoptions, features, underlying technologies, interfaces and usability, licenses & costs, data governance principles, but also integratability in WU's current systems landscape.

The latter could be also investigated by means of interviews with stakeholders at WU, whereas as a first part, research would be mostly based on online research and systmatically surveying and testing existing tools and platforms..

Available platforms and projects to establish such platforms include particularly, platforms  focused on Social Sciences research  (e.g. CESSDA [2], AUSSDA [3]), or platforms to pool data storing and processsing across (academic) research institutions (EOSC [4], VSC [5]) on a national or European level or many of other international similar initiatives e.g. ISPSR [6] in the US, NFDI in Germany [7] and many more... the goal of this theses is to exactly investiage the full landscape and state-of-the art in this space. Out of scope (or being subject  to a complementary investigation in another thesis) would be commercial cloud offerings, that are tailored to maintain, collect and share research data. 

We are looking for thesis research proposals that - looking at these starting points - come up with a plan to investigate, categorize and analyze the market as well as technical solutions in the emerging sector.

1. https://www.nature.com/articles/sdata201618

2. CESSDA
3. https://aussda.at/

4. https://www.cessda.eu/
5. https://www.eosc-portal.eu/
6 https://www.icpsr.umich.edu/icpsrweb/

7 https://www.ratswd.de/forschungsdaten/fdz

Background: Developing new methods for exploring and analyzing data in virtual and augmented reality presents many opportunities and challenges, both in terms of software development and design inspiration. There are various hardware options, from Google Cardboard to Oculus Rift. Taking part in this challenge demands programming skills as well as creativity. A basic VR or AR application for exploring a specific type of open data will be developed by the student. The use of a platform-independent kit such as A-Frame is essential, as the application will be compared in a small user study to its non-VR version in order to identify advantages and disadvantages of the visualization method implemented. Details will be discussed with supervisor.

Research problem: How can AR and VR be used to improve exploration of data?

Some References:

Butcher, Peter WS, and Panagiotis D. Ritsos. "Building Immersive Data Visualizations for the Web." Proceedings of International Conference on Cyberworlds (CW’17), Chester, UK. 2017. Teo, Theophilus, et al. "Data fragment: Virtual reality for viewing and querying large image sets." Virtual Reality (VR), 2017 IEEE. IEEE, 2017.

Millais, Patrick, Simon L. Jones, and Ryan Kelly. "Exploring Data in
Virtual Reality: Comparisons with 2D Data Visualizations." Extended
Abstracts of the 2018 CHI Conference on Human Factors in
Computing Systems. ACM, 2018.

Recent graph databases like Neo4j provide native support for querying paths. Classical SQL databases can offer similar functionality, but require more complex queries.

The research problem of this thesis is to analyze differences between SQL and NoSQL approaches in relation to the performance and scalability of temporal event-based data. Currently, there is little research in conducting this comparison for queries that are aiming exclusively at the analysis of process-like data structures (data that cannot be aggregated without losing its relation to event-order or time-relation). This is highly relevant since, to this point, most of the research approaches conduct this comparison for non-process-related data.

To this end, technology comparisons in the literature have to be analyzed, and practical performance has to be conducted by comparing a relational database (Postgres with the SQL dialect PL/pgSQL) with a graph database (Neo4j with CQL). A specific focus will be on how an increasing amount of data influences the performance of different types of queries for both types of databases.

References:

[1] Sharma, M., Sharma, V. D., & Bundele, M. M. (2018, November). Performance Analysis of RDBMS and No SQL Databases: PostgreSQL, MongoDB and Neo4j. In 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-5). IEEE.

[2] Hölsch, J., Schmidt, T., & Grossniklaus, M. (2017). On the performance of analytical and pattern matching graph queries in neo4j and a relational database. In EDBT/ICDT 2017 Joint Conference: 6th International Workshop on Querying Graph Structured Data (GraphQ).

[3] Holzschuher, F., & Peinl, R. (2013, March). Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j. In Proceedings of the Joint EDBT/ICDT 2013 Workshops (pp. 195-204).

The research domain of intrusion detection offers various techniques for identifying suspicious behaviour in information systems.

The research problem of this thesis is to find out to which extent techniques from intrusion detection can be used for process mining. To this end, intrusion detection techniques have to be analyzed with a focus on those building on Bayesian networks. The applicability should be investigated and demonstrated by using suitable techniques on the BPI Challenge event logs.

References

Dong, B., Chen, Z., Wang, H., Tang, L. A., Zhang, K., Lin, Y., ... & Chen, H. (2017, November). Efficient discovery of abnormal event sequences in enterprise security systems. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 707-715).

Xu, J., & Shelton, C. R. (2010). Intrusion detection using continuous time Bayesian networks. Journal of Artificial Intelligence Research, 39, 745-774.

Böhmer, K., & Rinderle-Ma, S. (2020). Mining association rules for anomaly detection in dynamic process runtime behavior and explaining the root cause to users. Information Systems, 90, 101438.

Background:

SPARQL is a query language and protocol that allows data publishers to expose query interfaces to Knowledge Graphs on the Web in a standardized manner, such that users who want can extract information using a language simiilar to SQL to access information from these knowledge graps, instead of having to download whole RDF dumps.

Availability of public  SPARQL query endpoints for open Knowledge Graphs (KGs) such as Wikidata [1] or DBpedia [2] is a severe problem, especially for smaller data publishers, if they want to provide query services to their datasets for concurrent clients on the internet.

Monitoring services like SPARQLES [3] have been put in place to assess this problem at scale regularly monitoring over 500 various public Knowledge graph query endpoints around the globe, and we have also confirmed similar observations in a recent study [4].

In order to assess the problems of keeping such servers alive, we want to investigate and classify the structure of the knowledge graphs behind these endpoints, and possible query loads that stress them. As a preliminary study to this end, we have already dumps of a large number of publicly available Linked Data KGs in a compressed queriable RDF format called HDT [5]. For some of the endpoints, even query logs are available [6].

Goal of the thesis:

The goal of this thesis is to do a systematic analysis of structure, and - if possible - problematic query loads for these, in order to classify and cluster "typical" patterns of publicly available KGs.

This ground work will help us to improve and further optimize novel methods for decentralized query answering using SPARQL, that we have recently developed [7], in order to balance the load of query processing between (concurrent) clients and servers.

Supervisor(s):

Axel Polleres/Amr Azzam

References:

1. Denny Vrandecic, Markus Krötzsch. Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10): 78-85 (2014)

2. Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick van Kleef, Sören Auer, Christian Bizer: DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web 6(2): 167-195 (2015)

3. Pierre-Yves Vandenbussche, Jürgen Umbrich, Luca Matteis, Aidan Hogan, Carlos Buil Aranda: SPARQLES: Monitoring public SPARQL endpoints. Semantic Web 8(6): 1049-1065 (2017)

4. Axel Polleres, Maulik R. Kamdar, Javier D. Fernández, Tania Tudorache, Mark A. Musen:

A more decentralized vision for Linked Data. Semantic Web 11(1): 101-113 (2020)

5. Javier D. Fernández, Miguel A. Martínez-Prieto, Claudio Gutiérrez, Axel Polleres, Mario Arias:

Binary RDF representation for publication and exchange (HDT). J. Web Semant. 19: 22-41 (2013)

6. Angela Bonifati, Wim Martens, Thomas Timm: An analytical study of large SPARQL query logs. VLDB J. 29(2): 655-679 (2020)

7. Amr, Azzam and Fernandez Garcia, Javier David and Maribel, Acosta and Polleres, Axel  (2020) SMART-KG: Hybrid Shipping for SPARQL Querying on the Web. Working Papers on Information Systems, Information Business and Operations, 2020/01. Department für Informationsverarbeitung und Prozessmanagement, Vienna. ISSN 2518-6809 https://epub.wu.ac.at/7428/

Background: Universities and public research institutions play an important role in the knowledge economy. Software written by researchers at universities is often open source, providing a valuable public good. Collaborations in open source software also link researchers across institutions.  Despite these facts, there has been little work to date measuring the contributions and collaborations of university affiliated developers of open source software.

Goal of the thesis: The goal of this thesis is to count contributions of universities to open source software and to map the network of their collaborations. The candidate will source and filter data from major open source software platforms such as GitHub and Gitlab. The candidate will apply data science methods to study the distributions and intensity of contributions across universities, and network science methods to analyze their collaborations.

References:
West, Joel, and Scott Gallagher. "Challenges of open innovation: the paradox of firm investment in open‐source software." R&D Management 36.3 (2006): 319-331.
Valiev, Marat, Bogdan Vasilescu, and James Herbsleb. "Ecosystem-level determinants of sustained activity in open-source projects: A case study of the PyPI ecosystem." Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2018.

Background: Open source software plays an import role in the digital economy. OSS is often written and maintained by teams of contributors, but the dynamics and collaboration networks of these teams are understudied. A better understanding of how OSS projects are organized can help us identify key developers, projects that rely too much on individuals, and the patterns of collaboration that predict success.

Goal of the thesis: The goal of this thesis is to create a tool to extract the collaboration histories of Git repositories, and to apply the tool to better understand the dynamics and collaboration networks of key OSS projects. The candidate will apply data science and network science methods to analyze data, creating an end to end pipeline that can be applied to any project tracked via Git.

References:
Joblin, Mitchell, Sven Apel, and Wolfgang Mauerer. "Evolutionary trends of developer coordination: A network approach." Empirical Software Engineering 22.4 (2017): 2050-2094.
Klug, Michael, and James P. Bagrow. "Understanding the group dynamics and success of teams." Royal Society open science 3.4 (2016): 160007.
Scholtes, Ingo, Pavlin Mavrodiev, and Frank Schweitzer. "From Aristotle to Ringelmann: a large-scale analysis of team productivity and coordination in Open Source Software projects." Empirical Software Engineering 21.2 (2016): 642-683.
Spadini, Davide, Maurício Aniche, and Alberto Bacchelli. "Pydriller: Python framework for mining software repositories." Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2018.

Recent years have seen an increasing availability of process execution data from several data sources. Process mining offers different analysis techniques to extract business insights from these data, known as event logs. An event log is a set of the executed process instances, i.e., cases. Each event in the event log represents an execution of process activity. The primary attributes associated with an event carry the control-flow information as execution timestamp, executed activity, and case identifier. Additionally, an event has different data attributes that describe business objects used for the process execution and process environment, e.g., resources, project name, location, cost..., etc.

The analysts use the process mining techniques to discover the control-flow model, identify the deviations using conformance checking techniques and provide recommendations and suggestions for process improvement.

Unfortunately, these techniques focus on the event process-attributes and ignoring the event data-context attributes. Analyzing the data-context attributes will improve the understanding of the process execution environment and the performed analysis quality.

The data-context attributes provide information about the business objects affected by executing the process instances. These attributes may be constant within a case or change over time in a case. Detecting the data-context attributes changing patterns will help understand what triggers these changes and how they affect the process variations. To detect the changing patterns, we need to identify the association and correlation rules between the data-context attributes. 

The thesis's objective is to build a tool that detects the associate and correlation rules between the log's event data-context attributes.

References:

[1] Pentland, B., et al. "Bringing context inside process research with digital trace data." Journal of the Association for Information Systems 21 (2020).

[2] Pegoraro, Marco, Merih Seran Uysal, and Wil MP van der Aalst. "Discovering Process Models from Uncertain Event Data." International Conference on Business Process Management. Springer, Cham, 2019.

[3] Motahari-Nezhad, Hamid Reza, et al. "Event correlation for process discovery from web service interaction logs." The VLDB Journal 20.3 (2011): 417-444.

[4] Hornik, Kurt, Bettina Grün, and Michael Hahsler. "arules-A computational environment for mining association rules and frequent item sets." Journal of Statistical Software 14.15 (2005): 1-25.

Process mining is devoted to the extraction of value from process data. An important research stream is process discovery: building a process model from the execution traces (event logs) only. The majority of efforts in this direction have been aimed at the discovery of procedural models, either in formal representations, e.g., Petri-nets, or higher level notations, e.g., BPMN.

Diagonal to the procedural paradigm is the declarative one: only the boundaries of the process are defined, not the specific control flow, allowing any action which does not contradict these constraints. This is a flexible representation which is gaining more and more attention, thanks to its power to facilitate the handling of complex and dynamic processes where the order of actions changes in each execution.

Following this demand, the Object Management Group (OMG) released the Case Management Model and Notation (CMMN), an industry standard notation for declarative specifications.

Research problem:

Despite being a standard since 2014, the research on CMMN is still scarce while the efforts are focused on notations like DECLARE or DCR graphs. These are well researched and known in the academic community, where they originated, but struggle to break into industry.

The thesis goal is to fill this gap developing a technique for automated discovery of CMMN models from event log data and its proof-of-concept software implementation.

Requirements:

Good programming skills

Initial references:

1- CMMN official specification: https://www.omg.org/cmmn/

2- Declarative process mining example (definition and use-case):

Wil M. P. van der Aalst, Maja Pesic, Helen Schonenberg: Declarative workflows: Balancing between flexibility and support. Computer Science - R&D 23(2): 99-113 (2009).

Marcella Rovani, Fabrizio Maria Maggi, Massimiliano de Leoni, Wil M. P. van der Aalst: Declarative process mining in healthcare. Expert Syst. Appl. 42(23): 9236-9251 (2015).

3 - The most recent survey on automated process discovery techniques (procedural and declarative):

Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Andrea Marrella, Massimo Mecella, Allar Soo: Automated Discovery of Process Models from Event Logs: Review and Benchmark. IEEE Trans. Knowl. Data Eng. 31(4): 686-705 (2019).

Background: 

Cybersecurity has become a key issue in modern information economies. Security incidents, such as intrusions, data breaches, denial of service attacks, and ransomware infections are routinely reported in the media and illustrate that many organizations struggle to cope with a growing variety and intensity of threats. The business impact of security incidents -- operational, financial, legal, and in terms of long-term losses of consumer confidence and trust -- can be significant. 

Security management aims to assess and control such information security risks based on known and newly identified vulnerabilities, weaknesses, and threats. Understanding the latter, i.e., adversaries and their intentions, goals, tactics, and techniques, is fundamental for risk assessment, selection of effective countermeasures, classification of attacks and for decisions on how to respond to them. To this end, various standards and tools exist that aim to facilitate the structured exchange of threat information, including, e.g., the Open Indicators of Compromise (OpenIOC) framework, Structured Threat Information Expression (STIX), Trusted Automated eXchange of Indicator Information (TAXII), the Open Vulnerability Assessment Language (OVAL), Malware Attribute Enumeration and Characterization (MAEC), the Vocabulary for Event Recording and Incident Sharing (VERIS), MISP, and MITRE’s ATT&CK framework for adversarial tactics and techniques.

Research problem:

The goal of this thesis is to review the state-of-the-art in threat intelligence sharing platforms, tools and practice. To this end, you will organize the landscape of existing tools, standards, and platforms, compare and contrast their coverage and functionality, and identify strengths, weaknesses, overlaps and opportunities for integration.

Starting Literature:

1. W. Tounsi, H. Rais, “A survey on technical threat intelligence in the age of sophisticated cyber attacks”, Computers & Security, vol. 72, pp. 212-233, 2018. Doi: https://doi.org/10.1016/j.cose.2017.09.001.

2. Wagner, Cynthia, et al. "Misp: The design and implementation of a collaborative threat intelligence sharing platform." Proceedings of the 2016 ACM on Workshop on Information Sharing and Collaborative Security. 2016.

3. V. Mavroeidis and S. Bromander, "Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence," 2017 European Intelligence and Security Informatics Conference (EISIC), Athens, 2017, pp. 91-98, doi: 10.1109/EISIC.2017.20.

 

Threat intelligence sharing standards, e.g., OpenIOC, STIX, TAXII, OVAL, MAEC, VERIS, ATT&CK, MISP etc.

Background:

Graphs are a natural representation in a wide range of real-world applications, such as social networks, biological interactions, linguistics, and computer networks. Graph analysis can provide a deeper understanding of such systems and help to predict their behavior. Several techniques have been developed to address tasks such as node classification, node recommendation, link prediction, and anomaly detection. More recently, techniques to transform graphs into a vector space representation, i.e., graph embeddings, have opened up new opportunities to tackle these tasks. 

Research problem:

Representing log data as graphs (e.g., network traffic, computer events, software behavior) allows an analyst to easier navigate and explore the events and their context. Spotting undesired, often rare events, inside graphs still requires a lot of manual work. Using word embedding techniques for anomaly detection in log data is a new field, which is in the focus of this thesis. You will: i) study existing word embedding approaches for log data (e.g., IP2Vec, Log2Vec), ii) reproduce and explore those approaches, iii) find interesting new application areas and datasets, and iv) extend existing embedding approaches if necessary and evaluate them on the new datasets.

Starting Literature:

1. M. Ring, A. Dallmann, D. Landes and A. Hotho, "IP2Vec: Learning Similarities Between IP Addresses," 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, 2017, pp. 657-666, doi: 10.1109/ICDMW.2017.93.

2. Fucheng Liu, Yu Wen, Dongxue Zhang, Xihe Jiang, Xinyu Xing, and Dan Meng. 2019. Log2vec: A Heterogeneous Graph Embedding Based Approach for Detecting Cyber Threats within Enterprise. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19). Association for Computing Machinery, New York, NY, USA, 1777–1794.

3. Tuulio, V 2020, “Review of popular word embedding models for event log anomaly detection purposes.” Master Thesis, University of Helsinki

 

Background: 

Semantic technologies and knowledge graphs have seen widespread adoption in the web industry, where they are increasingly used in applications such as contextual search, user profiling, social network modeling, and question-answering systems (e.g., virtual voice assistants). Industry uptake in the manufacturing domain has been less pronounced, but has been gaining momentum recently as indicated by a growing number of workshops and contributions on the topic. Hence, the potential of knowledge graphs to tackle the many and varied data integration, interoperability, and AI challenges that the manufacturing domain faces in the context of Industry 4.0 is increasingly being recognized.

Research problem:

Standardized schemas, conceptualizations, and vocabularies are a key building block in the process of creating (internal or public) industrial knowledge graphs, as they facilitate integration of data from various sources within the production environment. The goal of this thesis is to review the state-of-the art in semantic representation in Industry 4.0. To this end, you will conduct research into common categories of manufacturing data and (i) identify candidate standards, schemas, and vocabularies to represent data within these categories, (ii) discuss their potential to facilitate exchange of manufacturing data and/or to break down manufacturing data silos, and (iii) identify important gaps in available vocabularies necessary to build manufacturing knowledge graphs. You will thereby organize the landscape of existing standards, compare and contrast their coverage, identify overlaps and gaps, and analyze strengths and weaknesses.

Initial references:

1. Hogan, A., Blomqvist, E., Cochez, M., D’Amato, C., Melo, G.D., Gutiérrez, C., Gayo, J.E., Kirrane, S., Neumaier, S., Polleres, A., Navigli, R., Ngomo, A.N., Rashid, S.M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2020). Knowledge Graphs. ArXiv, abs/2003.02320. https://arxiv.org/abs/2003.02320

2. Varish Mulwad and Raghava Mutharaju. 2017. Industrial Knowledge Graphs Workshop 2017: co-located with the 9th International ACM Web Science Conference 2017 (Preface; part of Content). In Proceedings of the 2017 ACM on Web Science Conference (WebSci '17). ACM, New York, NY, USA, 423-424. DOI: https://doi.org/10.1145/3091478.3162382

3. I. Grangel-González, L. Halilaj, S. Auer, S. Lohmann, C. Lange and D. Collarana, "An RDF-based approach for implementing industry 4.0 components with Administration Shells," 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, 2016, pp. 1-8, doi: https://doi.org/10.1109/ETFA.2016.7733503.

4. P. Adolphs, H. Bedenbender, D. Dirzus, M. Ehlich, U. Epple, M. Hankel, et al., "Reference architecture model industrie 4.0 (rami4.0)", ZVEI and VDI Status Report, 2015. Available: https://www.zvei.org/fileadmin/user_upload/Presse_und_Medien/Publikationen/2016/januar/GMA_Status_Report__Reference_Archtitecture_Model_Industrie_4.0__RAMI_4.0_/GMA-Status-Report-RAMI-40-July-2015.pdf

 

Additional example references:

• N. Petersen, L. Halilaj, I. Grangel-González, S. Lohmann, C. Lange, and S. Auer. ”Realizing an RDF-based information model for a manufacturing company – A case study”. In: The Semantic Web – ISWC 2017, pages 350–366. Springer International Publishing, 2017. Available: https://link.springer.com/chapter/10.1007/978-3-319-68204-4_31

• Mörzinger, Benjamin, et al. "A large-scale framework for storage, access and analysis of time series data in the manufacturing domain." Procedia CIRP 67 (2018): 595-600.
  Available: https://www.sciencedirect.com/science/article/pii/S2212827117312131

• Annane, Amina, Nathalie Aussenac-Gilles, and Mouna Kamel. "BBO: BPMN 2.0 Based Ontology for Business Process Representation." 20th European Conference on Knowledge Management (ECKM 2019). 2019.

• Lemaignan, Severin, et al. "MASON: A proposal for an ontology of the manufacturing domain." IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06). IEEE, 2006.
  Available: https://academia.skadge.org/publis/Lemaignan2006.pdf [early work]

• Lin, Hsiao-Kang, and Jenny A. Harding. "A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration." Computers in Industry 58.5 (2007): 428-437.
  Available: https://www.sciencedirect.com/science/article/pii/S0166361506001837  [early work]

 

Background: 

Knowledge graphs describe real-world entities and their relationships in a graph structure, which is often constructed by integrating data automatically from existing structured repositories and deriving new knowledge through reasoning. Although there is no clear-cut definition of what constitutes a “knowledge graph”, the semantic foundations of such graphs have been a subject of intense research in the Semantic Web community (and other communities before) for a long time. Industry interest in the concept has grown rapidly since Google introduced its Knowledge Graph project in 2012. Since then, most other major online services, including Facebook, Bing, LinkedIn, and Amazon, have adopted the concept and built their own large-scale knowledge graphs, often to contextualize user behavior, provide entity search results, link distributed content assets, or understand and answer questions directly. Furthermore, a number of large-scale open knowledge graphs such as DBpedia and Wikidata have been created. Adoption in industrial applications and manufacturing in particular has been more limited, but a growing number of industry contributions to conferences and recent workshops suggest that their important role in facilitating data integration and innovative applications in the context of Industry 4.0 is increasingly being recognized.

Research problem:

In this thesis, you will develop a set of criteria to describe various types of (potential) industry knowledge graphs and their roles in manufacturing applications; based on this typology, you will conduct a systematic survey of use cases and reported industrial applications of knowledge graphs.

Initial references:

1. Hogan, A., Blomqvist, E., Cochez, M., D’Amato, C., Melo, G.D., Gutiérrez, C., Gayo, J.E., Kirrane, S., Neumaier, S., Polleres, A., Navigli, R., Ngomo, A.N., Rashid, S.M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2020). Knowledge Graphs. ArXiv, abs/2003.02320. https://arxiv.org/abs/2003.02320

2. Claudio Gutierrez and Juan F. Sequeda. “A Brief History of Knowledge Graph's Main Ideas: A tutorial”. International Semantic Web Conference 2019 http://knowledgegraph.today/paper.html

3. Gomez-Perez, Jose Manuel, et al. "Enterprise knowledge graph: An introduction." Exploiting linked data and knowledge graphs in large organizations. Springer, Cham, 2017. 1-14. https://pdfs.semanticscholar.org/70d2/d131861a49e5875fcaaeaf9478ac61c05734.pdf

4. Varish Mulwad and Raghava Mutharaju. 2017. Industrial Knowledge Graphs Workshop 2017: co-located with the 9th International ACM Web Science Conference 2017 (Preface; part of Content). In Proceedings of the 2017 ACM on Web Science Conference (WebSci '17). ACM, New York, NY, USA, 423-424. DOI: https://doi.org/10.1145/3091478.3162382

 

Additional example references:

• N. Petersen, L. Halilaj, I. Grangel-González, S. Lohmann, C. Lange, and S. Auer. ”Realizing an RDF-based information model for a manufacturing company – A case study”. In: The Semantic Web – ISWC 2017, 350–366. Springer, 2017. Available: https://link.springer.com/chapter/10.1007/978-3-319-68204-4_31

• Hubauer, Thomas, et al. "Use Cases of the Industrial Knowledge Graph at Siemens." International Semantic Web Conference (P&D/Industry/BlueSky). 2018. Available: http://ceur-ws.org/Vol-2180/paper-86.pdf

• Liebig, Thorsten, et al. "Building a Knowledge Graph for Products and Solutions in the Automation Industry." (2019). Available: https://openreview.net/pdf?id=ByldIMlEDV

• Mehdi, A., et al. "Towards Semantic Integration of Bosch Manufacturing Data." (2019). ISWC 2019 Industry Track. Available: http://ceur-ws.org/Vol-2456/paper79.pdf

• Banerjee, Agniva, et al. "Generating digital twin models using knowledge graphs for industrial production lines." Workshop on Industrial Knowledge Graphs, co-located with the 9th International ACM Web Science Conference 2017. Available: https://ebiquity.umbc.edu/paper/html/id/779/Generating-Digital-Twin-models-using-Knowledge-Graphs-for-Industrial-Production-Lines

• He, Longlong, and Pingyu Jiang. "Manufacturing Knowledge Graph: A Connectivism to Answer Production Problems Query With Knowledge Reuse." IEEE Access 7 (2019): 101231-101244. Available: https://ieeexplore.ieee.org/iel7/6287639/8600701/08777086.pdf

• Grangel-González, Irlán, et al. "Knowledge Graphs for Semantically Integrating Cyber-Physical Systems." International Conference on Database and Expert Systems Applications. Springer, Cham, 2018.
  Available: https://link.springer.com/chapter/10.1007/978-3-319-98809-2_12

 

Blockchain can be a component of a larger software system (Xu et al., 2019). Oracles are needed
for communication between the blockchain and the off-chain environment, including the other
components of the system. (Mühlberger et al., 2020) However, these oracles assume that the offchain
components are automated and provide well-defined APIs, which is not always the case.
Indeed, many companies still have legacy systems as part of their technology stack.
A possible way to automate interaction with the legacy systems is Robotic Process Automation.
Robotic Process Automation (RPA) is an emerging technology that employs software robots to
mimic human actions. (Syed et al., 2020) RPA, although essentially being a program, can interact
with the legacy systems as if it was a user, which makes it a promising solution to bridge the
communication gap between different components of a software system.

In this paper, the student will implement an oracle that would enable the communication between
the blockchain and a legacy system with the help of RPA. Note that programming skills are a strict
requirement.

For further questions, please contact Maxim Vidgof ( maxim.vidgof@wu.ac.at ).

Starting literature:
Xu, X., Weber, I., & Staples, M. (2019). Architecture for blockchain applications. Heidelberg:
Springer.
Mühlberger, R., Bachhofner, S., Ferrer, E. C., Di Ciccio, C., Weber, I., Wöhrer, M., & Zdun, U.
(2020, September). Foundational Oracle Patterns: Connecting Blockchain to the Off-chain World.
In International Conference on Business Process Management (pp. 35-51). Springer, Cham.
Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J., Ouyang, C., ter Hofstede, A. H. M.,
van de Weerd, I., Wynn, M. T. & Reijers, H. A. (2020). Robotic Process Automation: Contemporary
themes and challenges. Computers in Industry, 115, 103162.

Background:

Process discovery is a type of process mining that, given an event log, discovers the process model that best fits this log. Common techniques use sequences of executed activities which are explicitly represented in the event log. However, in processes where many activities are executed manually, the available information may contain other implicit activities executed by a resource or a team, which were not present in the event log. It is often the case that resources involved in the execution register their work in comments associated with the activity. These comments may enrich the event log and allow a more realist process model to be discovered.

Research problem:

The core research problem addresses how we can use the comments associated with activities to learn a better process model.

This thesis aims at using comments associated with activities to enrich the event log with activities retrieved from these comments and then learn a process model that is more representative of the actual business process.

Requirements:

The candidate must have previous knowledge on process mining. Further desirable requirements are pro-activity and self-organization. 

Initial references

1. Gupta, M., Agarwal, P., Tater, T., Dechu, S., & Serebrenik, A. (2020, July). Analyzing Comments in Ticket Resolution to Capture Underlying Process Interactions. In 4th International Workshop in Artificial Intelligence for Business Process Management. Springer.

2. Bala S., Revoredo K., de A.R. Gonçalves J.C., Baião F., Mendling J., Santoro F. (2017) Uncovering the Hidden Co-evolution in the Work History of Software Projects. In: Carmona J., Engels G., Kumar A. (eds) Business Process Management. BPM 2017. Lecture Notes in Computer Science, vol 10445. Springer, Cham. https://doi.org/10.1007/978-3-319-65000-5_10

3. Lindberg, A., Berente, N., Gaskin, J., & Lyytinen, K. (2016). Coordinating interdependencies in online communities: A study of an open source software project. Information Systems Research, 27(4), 751-772.

 

Background:
The quality of the outcomes of process mining techniques strongly relies on the quality of the
input data. Verifying the quality of the data before applying any process mining technique is
important to identify possible issues such as noise, missing values, etc. These issues should be
properly addressed to not compromise the findings of process mining techniques.

Research problem:
The core research problem addressed is: what are the data quality issues impacting process
mining techniques and how the area is dealing with them?
This thesis aims at doing a systematic review of the literature on techniques for dealing with data
quality issues in process mining and present open challenges.

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

Initial references
1. Wynn M.T., Sadiq S. (2019) Responsible Process Mining - A Data Quality Perspective. In:
Hildebrandt T., van Dongen B., Röglinger M., Mendling J. (eds) Business Process
Management. BPM 2019. Lecture Notes in Computer Science, vol 11675. Springer, Cham
2. Liu, C., Nitschke, P., Williams, S.P. et al. Data quality and the Internet of Things. Computing
102, 573–599 (2020)
3. S. Suriadi, R. Andrews, A.H.M. ter Hofstede, M.T. Wynn, Event log imperfection patterns for
process mining: Towards a systematic approach to cleaning event logs, Information Systems,
Volume 64, 2017, Pages 132-150, ISSN 0306-4379,https://doi.org/10.1016/j.is.2016.07.011.

Background:
Process monitoring is one of the phases in the Business Process Management (BPM) life cycle.
One possible task in this phase is process prediction, which predicts process indicators, such as
the remaining time for the process instance to finish or the next event to occur. These prediction
allow the anticipation of possible problems and they are usually based on activity information.
However, in many situations there is not enough information in order to achieve an accurate
prediction. Recent approaches have considered other sources of information combined with
activities information, such as sensors data or unstructured data. These information define the
context in which the instance is running. However the choice of context is done in a non
systematic way, often based on availability. Furthermore, the dependency of context with the
activities is not deeply analysed, possibly preventing its use by prediction.

Research problem:
The core research problem addressed is: how is contextual information being used for business
process prediction?
This thesis focuses on a literature review aimed at extracting knowledge about features which
describe context that is useful for process prediction.

Requirements:
The candidate must have previous knowledge on process mining and conceptual modeling.
Further desirable requirements are pro-activity and self-organization.

Initial references
1. Marquez-Chamorro, A. E., Resinas, M., & Ruiz-Cortes, A. (n.d.). Predictive
monitoring of business processes: a survey. IEEE Transactions on Services
Computing, 1. doi.org/10.1109/TSC.2017.2772256
2. J. Brunk, "Structuring Business Process Context Information for Process Monitoring and
Prediction," 2020 IEEE 22nd Conference on Business Informatics (CBI), Antwerp, Belgium, 2020,
pp. 39-48, doi: 10.1109/CBI49978.2020.00012.
3. Anastassiu, M., Santoro, F. M., Recker, J., & Rosemann, M. (2016). The quest
for organizational flexibility: Driving changes in business processes through
the identification of relevant context. Business Proc. Manag. Journal , 22(4),
763–790. doi.org/10.1108/BPMJ-01-2015-0007

Type of thesis: Master thesis

Guidelines for decision tables recommend to create tree-structured tables, which can be evaluated top-down by continuously choosing from the relevant conditions until a specific rule is reached. As a consequence, such a decision table is a representation of the decision tree. To achieve tree-structured and contracted tables, condition order optimization becomes relevant. Using a presentation order following natural order (e.g., from high numbers to low numbers) is recommended for DMN. This is also backed up by research showing that ordering information by rank or relevance allows for faster comprehension and better decisions in choice experiments. Furthermore, previous research shows that introducing colors in diagrams can lead to quicker visual search, reduced cognitive load, and ultimately higher levels of accuracy. However, this impact has not been empirically investigated in context of different decision table structures.

Therefore, the plan is to conduct an eye-tracking experiment which will investigate the impact of table structure and color on decision accuracy, efficiency, cognitive load, and search costs. 

For further questions, please contact Djordje Djurica (djordje.djurica@wu.ac.at)

J. Vanthienen, "Simplifying and Optimizing Tabular Decision Models," Business Rules Journal, vol. 12, 2011.

Object Management Group, "Decision Model and Notation (DMN) 1.2," 2018.

Moody, D. (2009), “The physics of notations: Toward a scientific basis for constructing visual notations in software engineering”

Reijers, H.A., Freytag, T., Mendling, J. and Eckleder, A. (2011), “Syntax highlighting in business process models”, Decision Support Systems

Type of thesis: Master thesis

A main purpose of DMN is to standardize different forms and types of decision tables. Still, the standard document allows for a variety of informationally equivalent design options of decision tables.

Concerning the layout of decision tables, the DMN standard document gives users freedom of choice and states that a decision table can be presented horizontally (rules-as-rows), vertically (rules-as-columns), or crosstab (rules composed from two input dimensions). Furthermore, the DMN standard does not give a concrete recommendation whether to prefer rules-as-rows or as columns. Although the placement of conditions and outputs in decision tables could likely be relevant for comprehension, it is unclear from cognitive research which design option is the superior one. While some authors like Vanthienen (2011) seem to prefer rules-as-columns and typically presents rules-as-columns decision tables in their papers, a book and proposal for decision tables by Halle and Goldberg (2009) offers only the rules-as-rows layout (Silver, 2016). Overall, the recommendations are inconsistent and previous studies have not taken this specific design decision into account.

Therefore, the aim of this thesis is to conduct an empirical investigation into which DMN decision table layout results in the best understandability. The plan is to conduct an experiment in which different layouts will be used.

For further questions, please contact Djordje Djurica (djordje.djurica@wu.ac.at)

B. Silver, DMN Method and Style: The Practitioner's Guide to Decision Modeling with Business Rules: Cody-Cassidy Press Aptos, 2016.

J. Vanthienen, "Simplifying and Optimizing Tabular Decision Models," Business Rules Journal, vol. 12, 2011.

B. v. Halle and L. Goldberg, The Decision Model: A Business Logic Framework Linking Business and Technology: Auerbach Publications, 2009.

Object Management Group, "Decision Model and Notation (DMN) 1.2," 2018.

Type of thesis: Master thesis

Research on model comprehension is extensive and focuses on many factors that are associated with the model itself and with user characteristics. Papers in this stream focused on the impact of the domain and modeling knowledge, learning style, cognitive styles, and even cultural factors. However, the literature is silent about the impact of affective states on working with conceptual models. It seems that the model reader is implicitly assumed to be a cold-tempered rational analyst who strictly adheres to facts. We know from research on decision-making in business that this assumption does not reflect reality. For these reasons, it is a significant omission of prior research on conceptual modeling that the impact of affective states has not been studied.

Therefore, the purpose of this thesis is to discover whether affective states can influence conceptual model comprehension. More specifically, the student will focus on investigating how negative emotion might influence model understanding. The plan is to conduct an experiment in which negative emotion will be induced and where this manipulation will be checked using a biophysical device.

For further questions, please contact Djordje Djurica (djordje.djurica@wu.ac.at)

Bogodistov, Y., & Moormann, J. (2019). Influence of Emotions on IT-driven Payment Process Design: Shorter, Simpler, and Riskier.

Figl, K. (2017). Comprehension of Procedural Visual Business Process Models: A Literature Review. Business and Information Systems Engineering. doi.org/10.1007/s12599-016-0460-2

Hibbeln, M., Jenkins, J. L., Schneider, C., Valacich, J. S., & Weinmann, M. (2017). How Is Your User Feeling? MIS Quarterly.

The field of knowledge management (KM) has reached maturity. A number of academic journals dedicated to publish research on knowledge management have become established, and some of them have considerable impact on management research in general. In addition to these, several conferences have been hosting the community of KM researchers for decades (e.g. ECKM), and a growing number of wide-ranging conferences devote streams to KM (e.g. HICSS). Even more, we see new KM conferences emerging that approach the topic in broader terms and emphasize the role of knowledge in/for other management fields (e.g. TAKE).

While academic KM journals have been regularly subject to bibliographic studies, a current comparative meta-analysis of (selected) KM conferences and leading journals is missing. Relevant questions included (but are not limited to): What are trends emerging topics? How does the coverage of KM journals and conferences differ? How does research on KM relate to other (management) fields? Who are key authors and what are dominant methodologies?

In this bachelor thesis, you will portray the historical development of the KM research field. Your main task is to conduct a bibliographic meta-analysis that compares leading KM journals and established, wide-ranging, and emerging KM conferences. Your task is to review these publication outlets in several respects and illustrate your findings (e.g. network analysis).

For further questions, please contact Florian Kragulj (florian.kragulj@wu.ac.at)

Initial references:

Dittes, S., Smolnik, S., Jennex, M. E., & Croasdell, D. T. (2016). Eleven Years of the Knowledge Management Track at HICSS: An Overview. International Journal of Knowledge Management (IJKM), 12(4), 51-61.

Lee, M. R., & Chen, T. T. (2012). Revealing research themes and trends in knowledge management: From 1995 to 2010. Knowledge-Based Systems, 28, 47-58.

Serenko, A. (2013). Meta-analysis of scientometric research of knowledge management: discovering the identity of the discipline. Journal of Knowledge Management, 17(5), 773-812.

Serenko, A., & Bontis, N. (2017). Global ranking of knowledge management and intellectual capital academic journals: 2017 update. Journal of Knowledge Management, 21(3), 675-692.

Serenko, A., & Dumay, J. (2015). Citation classics published in Knowledge Management journals. Part II: studying research trends and discovering the Google Scholar Effect. Journal of Knowledge Management, 19(6), 1335-1355.

The validation of prior learning is defined as a “process of confirmation by an authorised body that an individual has acquired learning outcomes measured against a relevant standard” (European Union, 2012, p. 5). Recently, the concept gained increasing interest in policy-making (European Union, 2012) and scholarly research (Colardyn and Bjornavold, 2004; Bjørnåvold, 2000a, 2000b) to make visible the prior learning of elderly, youth and migrants, among others (Souto-Otero and Villalba-Garcia, 2015). Putting the validation of prior learning into practice through specific implementations should make the knowledge, skills and competences of disadvantaged groups more to foster the integration into the labour market, and foster labour market mobility, among others.

The validation of prior learning can be distinguished into four phases, (1) the identification of knowledge, skills and competences through dialogue of particular experiences of an individual, the (2) documentation to make individual experiences visible, the (3) assessment of these experiences (Baartman et al., 2007; Baeten et al., 2008; Stenlund, 2010) and (4) the certification of the results of the assessment which may lead to a partial or full qualification (European Union, 2012, p. 5), that can be shown to employers.

Within this thesis, you will (1) review and (2) organize the existing research on the validation of prior learning. The guiding question for this thesis can be:

• What characterises research on the validation of prior learning?

 

The ideal candidate has an interest in knowledge-based management, (organizational) learning and is excited to work on an interdisciplinary and multilevel research, including education and psychological topics. A good command of English is necessary. Guidelines and requirements to write a bachelor thesis from the institute of information business apply. Two students can work on the topic simultaneously.

For further questions, please contact Florian Fahrenbach (florian.fahrenbach@wu.ac.at).

References

Baartman, L.K.J., Bastiaens, T.J., Kirschner, P.A. and van der Vleuten, C.P.M. (2007), “Evaluating assessment quality in competence-based education: A qualitative comparison of two frameworks”, Educational Research Review, Vol. 2 No. 2, pp. 114–129.

Baeten, M., Dochy, F. and Struyven, K. (2008), “Students' approaches to learning and assessment preferences in a portfolio-based learning environment”, Instructional Science, Vol. 36 No. 5/6, pp. 359–374.

Bjørnåvold, J. (2000a), “Making learning visible. identification, assessment and recognition of non-formal learning”, Vocational Training: European Journal, Vol. 22, pp. 24–32.

Bjørnåvold, J. (2000b), Making learning visible: Identification, assessment and recognition of non-formal learning in Europe, CEDEFOP reference document, Office for Official Publ. of the Europ. Communities, Luxembourg.

Colardyn, D. and Bjornavold, J. (2004), “Validation of Formal, Non-Formal and Informal Learning: policy and practices in EU Member States”, European Journal of Education, Vol. 39 No. 1, pp. 69–89.

European Union (2012), “Council recommendation of 20 December 2012 on the validation of non-formal and informal learning”, Official Journal of the European Union, pp. 1–5.

Souto-Otero, M. and Villalba-Garcia, E. (2015), “Migration and validation of non-formal and informal learning in Europe: Inclusion, exclusion or polarisation in the recognition of skills?”, International Review of Education, Vol. 61 No. 5, pp. 585–607.

Stenlund, T. (2010), “Assessment of prior learning in higher education: a review from a validity perspective”, Assessment & Evaluation in Higher Education, Vol. 35 No. 7, pp. 783–797.

There is a growing body of literature on how to enable knowledge sharing or knowledge transfer in organisations. However, a critical mind could ask if all that sharing of knowledge can affect the actual quality of shared knowledge. Also people may ignore parts of the knowledge they are confronted wih as they consider it wrong or irrelevant. The goal of this thesis is to find out if and how people assess knowledge they acquire or knowledge they are confronted with. When do people consider knowledge as relevant for them? What heuristics do people use to judge this relevance? Answers to this questions and others will be provided based on a review of the literature around knowledge sharing.

For further questions, please contact Clemens Kerschbaum (clemens.kerschbaum@wu.ac.at).