Wolfgang Mayer

Research Interests

Data management and data science Information extraction and fusion Knowledge representation and reasoning Machine learning Natural Language Processing

Prof Wolfgang Mayer

Associate Professor

School of Computer Science and Information Technology

College of Engineering and Information Technology

Available For Media Comment.

I am passionate about developing novel Artificial Intelligence technologies to help answer important questions in industry, healthcare, engineering, and Defense. My focus is on applied research where domain knowledge combined with data can provide solutions that mainstream “Big Data” and Machine Learning techniques cannot address. My expertise combines state-of-the-art machine learning & data analysis techniques, natural language processing technologies, and the “traditional” logic-based knowledge representation and reasoning techniques used for modelling, configuration, and diagnosis of technical systems.
I have a broad portfolio of applied research collaborations with individual businesses, government, and Defence organisations. My research has helped overcome data silos and provide intelligent tools for Asset Management, Health Care, Oil & Gas and steel industries, Waste Management, Sustainable Development, Transportation, the Government sector, and Defence. I have also developed intelligent tools for configuration and fault diagnosis in software systems, technical systems, and associated business processes. 
As a member of the Leadership Group in the Industrial AI Research Centre at UniSA, I am experienced in leading junior researchers and accustomed to collaborating with a variety of stakeholders in industry, government, and research institutions. My strong links with industry and Defence have resulted in a line of successful research projects on data platforms for engineering processes (AutoCRC), asset management (CIEAM CRC and FenEx CRC), scientific data collection (ANDS), health data collection (NECTAR), information management for law enforcement (Data to Decisions CRC), manufacturing (IMCRC), risk management in healthcare (DHCRC), multiple projects in the Defence context (information provenance for network analytics, behavioural simulation models, data-driven simulation processes, network analysis, narrative extraction) and several industry-funded projects related to data-driven optimisation in a variety of application areas. Overall, I have received ~$3.88M project funding in the last 5 years. Moreover, I was a Chief Investigator of 1 ARC Discovery Project grant, led the research directions of 5 postdoctoral fellows, graduated 8 PhD students, and published over 100+ scientific peer-reviewed papers.

Artificial Intelligence, Machine Learning, Natural Language Processing, Data Analysis, Model-based Reasoning and Diagnosis, Program Analysis and Debugging, Product and process configuration and optimisation, Data Fusion, Systems interoperability

Date Position Institution name
2022 - 2025 Associate Professor University of South Australia

Language Competency
English Can read, write, speak, understand spoken and peer review
German Can read, write, speak, understand spoken and peer review

Date Institution name Country Title
2007 - 2007 University of South Australia Australia PhD
2001 - 2001 TU Wien Austria Master of Computer Science

Date Title Institution name Country
— SCRUM Master & SCRUM Product Owner Scrum Alliance United States
— Diploma of Leadership and Management TAFE SA Australia

Year Citation
2025 Memon, U., Mayer, W., Selway, M., & Stumptner, M. (2025). Interoperability of AI-enhanced digital twins. Journal of Industrial Information Integration, 48(100961), 1-21.
DOI
2025 Maheshwari, S., Ochoa, J. J., Gu, N., Rameezdeen, R., Mayer, W., & Doe, R. M. (2025). Improving information sharing in Offsite Construction (OSC): a systematic literature review. Buildings, 15(5), 1-20.
DOI WoS1
2025 Gupta, G., Stanek, J., Mayer, W., & Grossmann, G. (2025). Clinical decision support systems redeployment - levels of matching between variables in different ecosystems. Studies in Health Technology and Informatics, 329, 1630-1631.
DOI
2025 Chen, B., Cao, Z., Mayer, W., Stumptner, M., & Kowalczyk, R. (2025). HCPI-HRL: Human Causal Perception and Inference-driven Hierarchical Reinforcement Learning. Neural Networks, 187, 13 pages.
DOI Scopus2 WoS2
2025 Boyd, C., Kleinig, T. J., Dawson, J., Patel, S., Mayer, W., & Bezak, E. (2025). Observer variability in CT Angiography Carotid Segmentation: assessing variability to set minimum clinical performance. Journal of Neuroimaging, 35(3, article no. e70058), 1-12.
DOI
2024 Xu, R., Mayer, W., Chu, H., Zhang, Y., Zhang, H. Y., Wang, Y., . . . Feng, Z. (2024). Automatic semantic modeling of structured data sources with cross-modal retrieval. Pattern Recognition Letters, 177, 7-14.
DOI Scopus6 WoS2
2024 Wickramaarachchige, M. W., Statsenko, L., Ochoa, J. J., Mayer, W., & Chileshe, N. (2024). Critical enablers for the development of sectoral innovation ecosystems: a conceptual framework. Cogent Business and Management, 11(1, article no. 2414858), 1-25.
DOI Scopus2 WoS1
2024 Meng, Z., Zhan, S., Xu, R., Mayer, W., Zhu, Y., Zhang, H. Y., . . . Feng, Z. (2024). Domain ontology-driven knowledge graph generation from text. ACM Transactions on Probabilistic Machine Learning, online(4), 1-18.
DOI
2024 Man, K., Chahl, J., Mayer, W., & Perera, A. (2024). The effects of different image parameters on human action recognition models trained on real and synthetic image data. IEEE Access, 12, 95223-95244.
DOI
2024 Paeedeh, N., Pratama, M., Masum, M. A., Mayer, W., Cao, Z., & Kowalczyk, R. (2024). Cross-domain few-shot learning via adaptive transformer networks. Knowledge-based Systems, 288(111458), 1-9.
DOI Scopus17 WoS14
2024 Paeedeh, N., Pratama, M., Wibirama, S., Mayer, W., Cao, Z., & Kowalczyk, R. (2024). Few-shot class incremental learning via robust transformer approach. Information Sciences, 675(120751), 1-21.
DOI Scopus2
2023 Morgan, R., Pulawski, S., Selway, M., Ghose, A., Grossmann, G., Mayer, W., . . . Kyprianou, R. (2023). Modelling temporal goals in runtime goal models. Data and Knowledge Engineering, 147(102205), 1-19.
DOI
2023 Wang, Y., Jiang, Q., Geng, Y., Hu, Y., Tang, Y., Li, J., . . . Feng, Z. (2023). SGMFQP: an ontology-based Swine Gut Microbiota Federated Query Platform. Methods, 212, 12-20.
DOI Scopus3 WoS2 Europe PMC2
2023 Zhang, Y., Xu, R., Lu, W., Mayer, W., Ning, D., Duan, Y., . . . Feng, Z. (2023). Multi-modal spatio-temporal knowledge graph of ship management. Applied Sciences, 13(16, article no. 9393), 1-17.
DOI Scopus6 WoS6
2023 Hao, Z., Mayer, W., Xia, J., Li, G., Qin, L., & Feng, Z. (2023). Ontology alignment with semantic and structural embeddings. Journal of Web Semantics, 78(100798), 1-14.
DOI Scopus21 WoS12
2022 Xu, J., Mayer, W., Zhang, H., He, K., & Feng, Z. (2022). Automatic semantic modeling for structural data source with the prior knowledge from knowledge base †. Mathematics, 10(24, article no. 4778), 1-19.
DOI Scopus4 WoS3
2022 Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, A. (2022). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216(article no. 119456), 1-21.
DOI Scopus408 WoS273
2021 Derakhshanfar, H., Ochoa, J. J., Kirytopoulos, K., Mayer, W., & Langston, C. (2021). A cartography of delay risks in the Australian construction industry: impact, correlations and timing. Engineering, Construction and Architectural Management, 28(7), 1952-1978.
DOI Scopus7 WoS7
2021 Tingey Holyoak, J., Pisaniello, J., Buss, P., & Mayer, W. (2021). The importance of accounting-integrated information systems for realising productivity and sustainability in the agricultural sector. International Journal of Accounting Information Systems, 41(100512), 1-19.
DOI Scopus22 WoS15
2021 Feng, Z., Mayer, W., He, K., Kwashie, S., Stumptner, M., Grossmann, G., . . . Huang, W. (2021). A schema-driven synthetic knowledge graph generation approach with extended graph differential dependencies (GDDxs). IEEE Access, 9(9311121), 5609-5639.
DOI Scopus10 WoS3
2019 Derakhshanfar, H., Ochoa, J. J., Kirytopoulos, K., Mayer, W., & Tam, V. W. Y. (2019). Construction delay risk taxonomy, associations and regional contexts: a systematic review and meta-analysis. Engineering, construction and architectural management, 26(10), 2364-2388.
DOI Scopus33 WoS30
2019 Mayer, W., & Jiang, B. (2019). JSS special issue program debugging and repair. Journal of systems and software, 158(110404), 1-2.
DOI
2018 Sikos, L. F., Stumptner, M., Mayer, W., Howard, C., Voigt, S., & Philp, D. (2018). Representing network knowledge using provenance-aware formalisms for cyber-situational awareness. Procedia computer science, 126, 29-38.
DOI Scopus18 WoS10
2017 Zhalama., Zhang, J., & Mayer, W. E. (2017). Weakening faithfulness: some heuristic causal discovery algorithms. Journal of data science and analytics, 3(2), 93-104.
DOI Scopus18
2017 Selway, M., Stumptner, M., Mayer, W., Jordan, A., Grossmann, G., & Schrefl, M. (2017). A conceptual framework for large-scale ecosystem interoperability and industrial product lifecycles. Data and knowledge engineering, 109, 85-111.
DOI Scopus26 WoS26
2017 Mayer, W., Jiang, B., Wong, W. E., & Tse, T. H. (2017). Message from the IWPD workshop organizers. Proceedings 2017 IEEE 28th International Symposium on Software Reliability Engineering Workshops Issrew 2017, xxvii.
DOI
2015 Selway, M., Grossmann, G., Mayer, W., & Stumptner, M. (2015). Formalising natural language specifications using a cognitive linguistic/configuration based approach. Information Systems, 54, 191-208.
DOI Scopus38 WoS28
2015 Grossmann, G., Mafazi, S., Mayer, W., Schrefl, M., & Stumptner, M. (2015). Change propagation and conflict resolution for the co-evolution of business processes. International journal of cooperative information systems, 24(1), 1-38.
DOI Scopus10 WoS5
2015 Mafazi, S., Grossmann, G., Mayer, W. E., Schrefl, M., & Stumptner, M. (2015). Consistent abstraction of business processes based on constraints. Journal on Data Semantics, 4(1), 59-78.
DOI Scopus4 WoS3
2015 Hallé, S., & Mayer, W. (2015). Message from the Program Chairs. Proceedings IEEE International Enterprise Distributed Object Computing Workshop Edocw, 2015-November, x.
DOI
2014 Selway, M., Mayer, W., & Stumptner, M. (2014). Semantic interpretation of requirements through cognitive grammar and configuration. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 8862, 496-510.
DOI Scopus19
2014 Cameron, A., Stumptner, M., Nandagopal, N., Mayer, W., & Mansell, T. (2014). Rule-based peer-to-peer framework for decentralised real-time service oriented architectures. Science of computer programming, 97(2), 202-234.
DOI Scopus22 WoS12
2011 Mayer, W., Stumptner, M., Killisperger, P., & Grossmann, G. (2011). A declarative framework for work process configuration. AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING, 25(2), 143-162.
DOI Scopus7 WoS4
2007 Mayer, W. E., & Stumptner, M. (2007). Model-based debugging : state of the art and future challenges. Electronic Notes in Theoretical Computer Science, 174(4), 61-82.
DOI Scopus40 WoS31
2003 Mayer, W. (2003). Modellbasierte diagnose von java programmen - Entwurf und implementierung eines wertbasierten modells. OGAI Journal Oesterreichische Gesellschaft Fuer Artificial Intelligence, 22(4), 16-19.
2002 Mayer, W., Stumptner, M., & Wotawa, F. (2002). Can AI help to improve debugging substantially? Automatic debugging and the Jade project. OGAI Journal Oesterreichische Gesellschaft Fuer Artificial Intelligence, 21(4), 18-22.
Scopus1
2002 Wotawa, F., Stumptner, M., & Mayer, W. E. (2002). Model bases debugging or how to diagnose programs automatically. Lecture notes in computer science, 2358, 746-757.
DOI Scopus41 WoS29

Year Citation
2026 Kularatne, S., Selway, M., Mayer, W., & Stumptner, M. (2026). Automating Perdurant Meta-Property Assignment Using GPT-4. In Event/exhibition information: 38th Australasian Joint Conference on Artificial Intelligence, AI 2025, Canberra, Australia, 01/12/2025 - 05/12/2025
Source details - Title: AI 2025: Advances in Artificial Intelligence (Vol. 16370 LNAI, pp. 40-53). Germany: Springer.
DOI
2025 Memon, U., Mayer, W., Stumptner, M., & Selway, M. (2025). Challenges in composite Digital Twin models and their impact on interoperability. In M. Dassisti, K. Madani, & H. Panetto (Eds.), Event/exhibition information: 5th International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024, Porto, 21/11/2024-22/11/2024
Source details - Title: Innovative Intelligent Industrial Production and Logistics : 5th International Conference, IN4PL 2024, Porto, Portugal, November 21–22, 2024, Proceedings, Part II Communications in Computer and Information Science (Vol. 2373, pp. 413-425). Cham, Switzerland: Springer.
DOI Scopus2 WoS1
2025 Yang, S., Liu, Z., Mayer, W., Ding, N., Wang, Y., Huang, Y., . . . Feng, Z. (2025). ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources. In M. Barhamgi, H. Wang, & X. Wang (Eds.), Event/exhibition information: 25th International Conference on Web Information Systems Engineering, WISE 2024, Doha, 02/12/2024-05/12/2024
Source details - Title: International Conference on Web Information Systems Engineering (Vol. 15439 LNCS, pp. 284-298). Singapore: Springer.
DOI Scopus3
2024 Clarke, T. J., Gwilt, I., Zucco, J., Mayer, W., & Smith, R. T. (2024). Superpowers in the metaverse: augmented reality enabled X-ray vision in immersive environments. In V. Geroimenko (Ed.), Source details - Title: Augmented and Virtual Reality in the Metaverse (Vol. Part F2842, pp. 283-309). Switzerland: Springer.
DOI Scopus2
2023 Luo, J., Zhang, Y., Wang, Y., Mayer, W., Ding, N., Li, X., . . . Feng, Z. (2023). A reinforcement learning-based approach for continuous knowledge graph construction. In Z. Jin (Ed.), Source details - Title: Knowledge Science, Engineering and Management : 16th International Conference, KSEM 2023, Guangzhou, China, August 16–18, 2023, Proceedings, Part IV (Vol. 34, pp. 418-429). Cham, Switzerland :: Springer Nature Switzerland AG,.
DOI
2023 Dawoud, A., Mahala, G., Islam, C., Mayer, W., Ghose, A., Babar, M. A., . . . Grossmann, G. (2023). A Goal-Driven Approach to Support Decision-Making with Incomplete Information in Cyber Operations. In C. Cabanillas, & F. Pérez (Eds.), Intelligent Information Systems (Vol. 477 LNBIP, pp. 77-85). Cham, Switzerland :: Springer International Publishing.
DOI
2021 Mayer, W., & Wotawa, F. (2021). Artificial intelligence methods for software debugging. In M. Kalech, R. Abreu, & M. Last (Eds.), Source details - Title: Artificial Intelligence Methods for Software Engineering (pp. 401-435). US: World Scientific.
DOI
2021 Mayer, W., & Wotawa, F. (2021). Artificial Intelligence Methods for Software Debugging. In Artificial Intelligence Methods for Software Engineering (pp. 401-435).
DOI
2020 Smith, R. T., Clarke, T. J., Mayer, W., Cunningham, A., Matthews, B., & Zucco, J. E. (2020). Mixed reality interaction and presentation techniques for medical visualisations. In P. M. Rea (Ed.), Source details - Title: Biomedical Visualisation (Vol. 1320, pp. 123-139). Switzerland: Springer.
DOI Scopus26 WoS17 Europe PMC8
2019 Philp, D., Chan, N., & Mayer, W. (2019). Network path estimation in uncertain data via entity resolution. In T. D. Le (Ed.), Event/exhibition information: 17th Australasian Conference on Data Mining, AusDM 2019, Adelaide, Australia, 02/12/2019-05/12/2019
Source details - Title: Data mining (Vol. 1127 CCIS, pp. 196-207). Singapore: Springer.
DOI Scopus1 WoS1
2018 Mayer, W., Grossmann, G., Selway, M., Stanek, J., & Stumptner, M. (2018). Variety management for big data. In T. Hope, B. Humm, & A. Reibold (Eds.), Source details - Title: Semantic applications: methodology, technology, corporate use (pp. 47-62). Germany: Springer.
DOI Scopus5
2018 Sikos, L. F., Philp, D., Howard, C., Voigt, S., Stumptner, M., & Mayer, W. (2018). Knowledge representation of network semantics for reasoning-powered cyber-situational awareness. In L. F. Sikos (Ed.), Source details - Title: AI in cybersecurity (Vol. 151, pp. 19-45). Switzerland: Springer.
DOI Scopus11
2015 Jordan, A., Selway, M., Grossmann, G., Mayer, W., & Stumptner, M. (2015). Ontology-Based Process Modelling for Design. In Design Computing and Cognition 14 (pp. 551-569). Springer International Publishing.
DOI Scopus1
2014 Tiihonen, J., Mayer, W., Stumptner, M., & Heiskala, M. (2014). Configuring services and processes. In A. Felfernig (Ed.), Source details - Title: Knowledge-based configuration: from research to business cases (pp. 251-260). Massachusetts: Morgan Kaufmann.
DOI Scopus7
2010 Mayer, W. E., Pucel, X. T., & Stumptner, M. (2010). Diagnosing component interaction errors from abstract event traces. In L. Li, & J. Jiuyong (Eds.), Event/exhibition information: 23rd Australasian Joint Conference on Artificial Intelligence, Adelaide, South Australia, 07/12/2010-10/12/2010
Source details - Title: AI 2010 : advances in artificial intelligence : 23rd Australasian joint conference, Adelaide, Australia, December 2010 : proceedings (Vol. 6464 LNAI, pp. 496-505). Berlin, Germany: Springer.
DOI Scopus1

Year Citation
2026 Kularatne, S., Mayer, W., & Stumptner, M. (2026). Automating OntoClean Ontology Verification. In Lecture Notes in Computer Science Vol. 15706 LNAI (pp. 283-294). Springer Nature Singapore.
DOI Scopus2
2026 Kularatne, S., Mayer, W., & Stumptner, M. (2026). Automating OntoClean - Subsumption Hierarchy Construction. In Lecture Notes in Computer Science Vol. 15706 LNAI (pp. 295-302). Springer Nature Singapore.
DOI
2025 Kularatne, S., Mayer, W., Selway, M., & Stumptner, M. (2025). Meta-Property Constraints for Validating Perdurants. In Frontiers in Artificial Intelligence and Applications Vol. 409 (pp. 181-195). IOS Press.
DOI Scopus1
2025 Cunningham, A., Wark, S., Volmer, B., Nowina-Krowicki, M., Mayer, W., Underwood, S., . . . Shekh, S. (2025). Examining the Effectiveness of Storyline Visualisation for Understanding Wargame Scenarios: A Pilot Study. In Proceedings of the 36th Australasian Conference on Human Computer Interaction Ozchi 2024 (pp. 647-653). ACM.
DOI
2024 Lee, S., Efatmaneshnik, M., James, A., Mayer, W., Smith, J., & Grabert, T. (2024). Fuzzy rule-based quantitative framework for system testability measurement. In 2024 IEEE International Symposium on Systems Engineering (ISSE) (pp. 1-4). US: IEEE.
DOI
2024 Gupta, G., Stanek, J., Mayer, W., & Grossmann, G. (2024). Adaptive semantic framework for CDSS to a new environment. In D. Abramson, S. Vassar, & C. Jin (Eds.), ACM International Conference Proceeding Series (pp. 75-77). US: ACM.
DOI Scopus1
2024 Liu, X., Li, C., Qin, L., Li, J., Wang, J., Feng, Z., & Mayer, W. (2024). Overlapping entity relation extraction based on syntactic dependency tree and multi-dimensional corner marking strategy. In D. -S. Huang, Z. Si, & C. Zhang (Eds.), Advanced Intelligent Computing Technology and Applications20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part III Vol. 3 (pp. 214-225). Singapore: Springer.
DOI
2024 Li, J., Wang, J., Li, C., Liu, X., Feng, Z., Qin, L., & Mayer, W. (2024). Document-level relation extraction with additional evidence and entity type information. In D. -S. Huang, Z. Si, & C. Zhang (Eds.), Advanced Intelligent Computing Technology and Applications20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part III Vol. 3 (pp. 226-237). Singapore: Springer.
DOI
2023 Ding, N., Mayer, W., Geng, Y., Duan, Y., & Feng, Z. (2023). Generative semantic modeling for structured data source with large language model. In Proceedings - 2023 IEEE International Conference on High Performance Computing and Communications, Data Science and Systems, Smart City and Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2023 (pp. 1148-1152). US: IEEE.
DOI
2023 McDade, J., Drogemuller, A., Jing, A., Ireland, N., Walsh, J., Thomas, B., . . . Cunningham, A. (2023). CADET: a collaborative agile data exploration tool for mixed reality. In Proceedings - 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022 (pp. 899-900). US: IEEE.
DOI Scopus3 WoS1
2023 Du, Z., Xu, D., Huang, D., Hu, Y., He, K., Wang, C., . . . Feng, Z. (2023). An ontology-based method for heterogeneous data governance with MFI and MDR. In Proceedings - 2023 IEEE International Conference on High Performance Computing and Communications, Data Science and Systems, Smart City and Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2023 (pp. 1106-1113). US: IEEE.
DOI
2023 Clarke, T. J., Mayer, W., Zucco, J. E., Drogemuller, A., & Smith, R. T. (2023). Volumetric X-ray vision using illustrative visual effects. In Proceedings - 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023 (pp. 769-771). US: IEEE.
DOI
2023 Clarke, T. J., Mayer, W., Zucco, J. E., & Smith, R. T. (2023). Generating pseudo random volumes for volumetric research. In Proceedings - 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023 (pp. 266-270). US: IEEE.
DOI
2022 Dias Guimaraes, G., Gu, N., Gomes, V., Ochoa Paniagua, J., Rameezdeen, R., Mayer, W., & Kim, K. (2022). Data, stakeholders and environmental assessment: a BIM-enabled approach to designing-out construction and demolition waste. In Post-carbon, Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) Vol. 2 (pp. 587-596). Australia: CAADRIA.
DOI
2022 Morgan, R., Pulawski, S., Selway, M., Mayer, W., Grossmann, G., Stumptner, M., . . . Kyprianou, R. (2022). Modeling rates of change and aggregations in runtime goal models. In J. Ralyte (Ed.), Conceptual Modeling (Er 2022) Vol. 13607 LNCS (pp. 397-412). Switzerland: Springer.
DOI Scopus2
2022 Clarke, T. J., Mayer, W., Zucco, J. E., Matthews, B. J., & Smith, R. T. (2022). Adapting VST AR X-ray vision techniques to OST AR. In IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 495-500). US: IEEE.
DOI Scopus5 WoS4
2022 Dai, Y., Zhang, S., Tang, Y., Mayer, W., Li, J., Li, H., . . . Feng, Z. (2022). GMFQP: an Ontology-mediated gut microbiota federated query platform. In D. Adjeroh (Ed.), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 (pp. 730-735). US: IEEE.
DOI WoS2
2022 Xu, R., Mayer, W., Wang, Y., Zhang, H. Y., Ning, D., Duan, Y., . . . Zaiwen, F. (2022). Automatic semantic modeling by cross-modal retrieval. In Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022 (pp. 2142-2150). US: IEEE.
DOI Scopus1
2022 Chu, H., Wu, W., Mayer, W., Cheng, D., Zhang, H. Y., & Feng, Z. (2022). Dynamic Semantic Modeling of Structural Data Sources. In Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022 (pp. 74-81). US: IEEE.
DOI
2021 Feng, Z. W., Xu, J. K., Mayer, W., Huang, W. Y., He, K. Q., Stumptner, M., . . . Ling, L. (2021). Automatic semantic modeling for structural data source with the prior knowledge from knowledge graph. In Proceedings 2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021 (pp. 2034-2041). US: IEEE.
DOI Scopus5
2021 Feng, Z., Mayer, W., Stumptner, M., Grossmann, G., Kwashie, S., Ning, D., & He, K. (2021). ASMaaS: automatic semantic modeling as a service. In N. Atukorala (Ed.), Proceedings - 2021 IEEE World Congress on Services, SERVICES 2021 (pp. 33-40). US: IEEE Computer Society Press.
DOI Scopus1
2021 Zhang, S., Tang, Y., Yan, J., Li, L., Li, T., Li, J., . . . He, K. (2021). A graph-based approach for integrating biological heterogeneous data based on connecting ontology. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, . . . Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 600-607). US: IEEE.
DOI Scopus6 WoS1
2021 Selway, M., Stumptner, M., & Mayer, W. (2021). Towards formalisation of concept descriptions and constraints. In F. Neuhaus, & B. Brodaric (Eds.), Formal Ontology in Information Systems Vol. 344 (pp. 18-32). Netherlands: IOS Press.
DOI Scopus2
2019 Zhalama., Zhang, J., Eberhardt, F., Mayer, W., & Li, M. J. (2019). ASP-based discovery of semi-markovian causal models under weaker assumptions. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2019-August (pp. 1488-1494). China: International Joint Conferences on Artificial Intelligence.
DOI Scopus8 WoS3
2019 Derakhshanfar, H., Elmualim, A., Paniagua, J. O., Mayer, W., & Gunawan, I. (2019). Projects Risk Management Using Artificial Neural Networks Based on Lessons Learned. In Proceedings of the CRIOCM 2017 22nd International Conference on Advancement of Construction Management and Real Estate (pp. 69-76). Australia: CRIOCM 2017 Organising Committee.
2019 Derakhshanfar, H., Elmualim, A., Paniagua, J. O., Mayer, W., & Gunawan, I. (2019). Causes of Delays in Iranian Gas and Combined Cycle Power Plant Projects. In Proceedings of the CRIOCM 2017 22nd International Conference on Advancement of Construction Management and Real Estate (pp. 61-68). Australia: CRIOCM 2017 Organising Committee.
2018 Sikos, L. F., Stumptner, M., Mayer, W., Howard, C., Voigt, S., & Philp, D. (2018). Automated reasoning over provenance-aware communication network knowledge in support of cyber-situational awareness. In W. Liu, F. Giunchiglia, & B. Yang (Eds.), International conference on knowledge science, engineering and management KSEM 2018, proceedings Vol. 11062 LNAI (pp. 132-143). Switzerland: Springer Nature Switzerland AG.
DOI Scopus13 WoS7
2018 Stumptner, M., Mayer, W., Grossmann, G., Liu, J., Li, W., Casanovas, P., . . . Bainbridge, B. (2018). An Architecture for Establishing Legal Semantic Workflows in the Context of Integrated Law Enforcement. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10791 (pp. 124-139). Switzerland: Springer.
DOI Scopus5
2018 Li, W., Feng, Z., Mayer, W., Grossmann, G., Kashefi, A. K., & Stumptner, M. (2018). FEDSA: A data federation platform for law enforcement management. In Proceedings - 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, EDOC 2018 (pp. 21-27). US: IEEE.
DOI Scopus4 WoS3
2018 Feng, Z., Mayer, W., Stumptner, M., Grossmann, G., & Huang, W. (2018). Relationship matching of data sources: A graph-based approach. In Advanced information systems engineering Vol. 10816 LNCS (pp. 539-553). Estonia: Springer.
DOI Scopus2 WoS1
2018 Mayer, W., Casanovas, P., Stumptner, M., De Koker, L., & Mendelson, D. (2018). Semantic workflows in law enforcement investigations and legal requirements. In CEUR Workshop Proceedings Vol. 2049 (pp. 51-63). Germany: Rheinisch-Westfaelische Technische Hochschule Aachen.
2018 Selway, M., Owen, K. R., Dexter, R. M., Grossmann, G., Mayer, W., & Stumptner, M. (2018). Automated techniques for generating behavioural models for constructive combat simulations. In Data and decision sciences in action (pp. 103-115). Switzerland: Springer.
DOI WoS2
2018 Sikos, L. F., Philp, D., Voigt, S., Howard, C., Stumptner, M., & Mayer, W. (2018). Provenance-aware LOD datasets for detecting network inconsistencies. In CEUR Workshop Proceedings Vol. 2317 (pp. 1-6). Germany: Lehrstuhl Informatik V.
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2017 Mayer, W., Stumptner, M., Casanovas, P., & De Koker, L. (2017). Towards a linked information architecture for integrated law enforcement. In M. Poblet, P. Casanovas, & E. Plaza (Eds.), CEUR Workshop Proceedings Vol. 1897 (pp. 15-27). Germany: RWTH Aachen.
Scopus2
2017 Zhalama., Zhang, J., Eberhardt, F., & Mayer, W. (2017). SAT-based causal discovery under weaker assumptions. In Uncertainty in Artificial Intelligence: Proceedings of the 33rd Conference, UAI 2017 (pp. 1-10). US: AUAI Press.
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2015 Selway, M., Stumptner, M., Mayer, W. E., Jordan, A. A., Grossmann, G., & Schrefl, M. (2015). A conceptual framework for large-scale ecosystem interoperability. In P. Johannesson (Ed.), Conceptual modeling : 34th International Conference, ER 2015, Stockholm, Sweden, October 19–22, 2015, proceedings Vol. 9381 (pp. 287-301). New York: Springer.
DOI Scopus12 WoS8
2015 Wang, X., Tinati, R., Mayer, W., Rowland Campbell, A., Tiropanis, T., Brown, I., . . . Koronios, A. (2015). Building a web observatory for South Australian government: supporting an age friendly population. In 3rd International Workshop on Building Web Observatories (BWOW) (pp. 1-10). United Kingdom: University of Southampton.
2015 Selway, M., Stumptner, M., Mayer, W., Jordan, A., Grossman, G., & Schrefl, M. (2015). Multilevel mapping of ecosystem descriptions. In C. Dubruyne (Ed.), OTM 2015: On the Move to Meaningful Internet Systems: OTM 2015 Conferences Vol. 9415 (pp. 257-266). Switzerland: Springer International Publishing.
DOI
2014 Jordan, A., Mayer, W., & Stumptner, M. (2014). Multilevel modelling for interoperability. In C. Atkinson (Ed.), CEUR Workshop Proceedings Vol. 1286 (pp. 93-102). Germany: Rheinisch-Westfälische Technische Hochschule.
Scopus1
2014 Jordan, A. A., Selway, M. R., Mayer, W. E., Grossmann, G., & Stumptner, M. (2014). An ontological core for conformance checking in the engineering life-cycle. In P. Garbacz, & O. Kutz (Eds.), Formal Ontology in Information Systems : Proceedings of the Eighth International Conference (FOIS 2014) Vol. 267 (pp. 358-371). US: IOS Press.
DOI Scopus2
2014 Mafazi, S., Mayer, W. E., & Stumptner, M. (2014). Conflict resolution for on-the-fly change propagation in business processes. In Conferences in research and practice in information technology series Vol. 154 (pp. 39-48). Sydney, NSW: Australian Computer Society.
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2014 Jordan, A., Selway, M., Grossmann, G., Mayer, W., & Stumptner, M. (2014). Re-engineering the ISO 15926 Data Model: A Multi-level Metamodel Perspective. In A. Lomuscio, S. Nepal, F. Patrizi, B. Benatallah, & I. Brandic (Eds.), SERVICE-ORIENTED COMPUTING - ICSOC 2013 WORKSHOPS Vol. 8377 (pp. 248-255). Switzerland: SPRINGER-VERLAG BERLIN.
DOI Scopus2 WoS1
2014 Selway, M., Mayer, W., & Stumptner, M. (2014). Semantic interpretation of requirements through cognitive grammar and configuration. In Proceedings 13th Pacific Rim International Conference on Artificial Intelligence Vol. 8862 (pp. 496-510). Germany: Springer.
DOI WoS1
2014 Mafazi, S., Grossmann, G., Mayer, W., & Stumptner, M. (2014). Towards a reference architecture for the co-evolution of business processes. In 2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations (EDOCW) (pp. 389-396). US: IEEE.
DOI Scopus4
2013 Selway, M., Grossmann, G., Mayer, W., & Stumptner, M. (2013). Formalising natural language specifications using a cognitive linguistics/configuration based approach. In Proceedings : 17th IEEE International Enterprise Distributed Object Computing Conference : EDOC 2013 (pp. 59-68). California: IEEE Computer Society.
DOI Scopus5
2013 Mayer, W., Stumptner, M., Grossmann, G., & Jordan, A. (2013). Semantic interoperability in the oil and gas industry: a challenging testbed for semantic technologies. In 2013 AAAI fall symposium series: semantics for big data: AAAI technical report FS-13-04 Vol. FS-13-04 (pp. 40-43). US: Association for the Advancement of Artificial Intelligence.
Scopus4
2013 Selway, M., Mayer, W., & Stumptner, M. (2013). Configuring domain knowledge for natural language understanding. In M. Aldanondo, & A. Falkner (Eds.), Proceedings of the 15th International Configuration Workshop Vol. 1128 (pp. 63-70). France: École des Mines d'Albi-Carmaux.
Scopus1
2013 Mafazi, S., Grossmann, G., Mayer, W., & Stumptner, M. (2013). On-the-Fly Change Propagation for the Co-evolution of Business Processes. In R. Meersman, H. Panetto, T. Dillon, J. Eder, Z. Bellahsene, N. Ritter, . . . D. Dou (Eds.), ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2013 CONFERENCES Vol. 8185 (pp. 75-93). Germany: SPRINGER-VERLAG BERLIN.
DOI Scopus11 WoS7
2013 Cameron, A., Stumptner, M., Nandagopal, N., Mayer, W., & Mansell, T. (2013). A rule-based platform for distributed real-time SOA with application in defence systems. In Military Communications and Information Systems Conference (MilCIS), 2013 (pp. 1-7). US: IEEE.
DOI Scopus2
2013 Cameron, A., Stumptner, M., Nandagopal, N., Mayer, W. E., & Mansell, T. (2013). Performance analysis of a rule-based SOA component for real-time applications. In SAC '13: Proceedings of the 28th Annual ACM symposium on applied computing (pp. 1877-1884). New York, NY: Association for Computing Machinery.
DOI Scopus1
2012 Cameron, A., Stumptner, M., Nandagopal, N., Mayer, W., & Mansell, T. (2012). Rule-based control of decentralised asynchronous SOA for real-time applications. In Proceedings of the 2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery : CyberC 2012 (pp. 241-248). US: IEEE.
DOI
2012 Jordan, A., Grossmann, G., Mayer, W., Selway, M., & Stumptner, M. (2012). On the application of software modelling principles on ISO 15926. In MOTPW'12 Proceedings of the Modelling of the Physical World Workshop (pp. 1-6). US: Association for Computing Machinery.
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2012 Mayer, W., Friedrich, G., & Stumptner, M. (2012). On computing correct processes and repairs using partial behavioral models. In L. Raedt (Ed.), ECAI 2012 : 20th European Conference on Artificial Intelligence : proceedings Vol. 242 (pp. 582-587). Netherlands: IOS Press.
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2012 Mafazi, S., Mayer, W., Grossmann, G., & Stumptner, M. (2012). A knowledge-based approach to the configuration of business process model abstractions. In A. H. M. Hofstede (Ed.), Knowledge-intensive business processes: 1st International Workshop, KiBP 2012: proceedings Vol. 861 (pp. 60-74). Italy: The editors.
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2011 Beeson, M., Halcomb, J., & Mayer, W. (2011). Inconsistencies in the process specification language (PSL). In P. Höfner, A. McIver, & G. Struth (Eds.), CEUR Workshop Proceedings Vol. 760 (pp. 9-19). UK: NICTA ; University of Sheffield.
Scopus1
2010 Mayer, W. E., Friedrich, G., & Stumptner, M. (2010). Diagnosis of service failures by trace analysis with partial knowledge. In M. Maglio, & P. P (Eds.), Proceedings of the 8th international conference, ICSOC 2010 Vol. 6470 LNCS (pp. 334-349). Germany: Springer.
DOI Scopus21 WoS13
2010 Grossmann, G., Stumptner, M., Mayer, W. E., & Barlow, M. (2010). A service oriented architecture for data integration in asset management. In K. Dimitris (Ed.), Engineering Asset Lifecycle Management - Proceedings of the 4th World Congress on Engineering Asset Management, WCEAM 2009 (pp. 785-795). UK: Springer.
DOI Scopus2
2010 Friedrich, G., Mayer, W. E., & Stumptner, M. (2010). Diagnosing process trajectories under partially known behavior. In C. Coelho, & H. Helder (Eds.), ECAI 2010 : 19th European Conference on Artificial Intelligence, 16-20 August 2010, Lisbon, Portugal : including Prestigious Applications of Artificial Intelligence (PAIS-2010) : proceedings Vol. 215 (pp. 111-116). [Amsterdam] ; Washington, D.C.: IOS Press.
DOI Scopus4 WoS4
2009 Mayer, W. E., Thiagarajan, R. K., & Stumptner, M. (2009). Service composition as generative constraint satisfaction. In E. Damiani (Ed.), 2009 IEEE International Conference on Web Services (ICWS 2009) (pp. 888-895). United States: IEEE.
DOI Scopus24 WoS12
2009 Abreu, R., Mayer, W. E., Stumptner, M., & Gemund, A. (2009). Refining spectrum-based fault localization rankings. In Proceedings of the 2009 ACM symposium on applied computing (pp. 409-414). USA: ACM.
DOI Scopus32
2009 Thiagarajan, R. K., Mayer, W. E., & Stumptner, M. (2009). A generative framework for service process composition. In Service-oriented computing : 7th international joint conference, ICSOC-Servicewave 2009, Stockholm, Sweden, November 24-27, 2009 : proceedings Vol. 5900 LNCS (pp. 358-363). New York: Springer.
DOI Scopus3 WoS2
2009 Pucel, X. T., Mayer, W. E., & Stumptner, M. (2009). Diagnosability analysis without fault models. In F. Frisk, & E. Erik (Eds.), 20th international workshop on principles of diagnosis (DX-09). Sweden: Linkoping University.
2009 Mayer, W. E., Bettex, M., Stumptner, M., & Falkner, A. (2009). On solving complex rack configuration problems using CSP methods. In S. Stumptner, & M. Markus (Eds.), Proceedings of the IJAI-09 workshop on configuration (ConfWS-09). USA: International Joint Conference on Artificial Intelligence.
2009 Mayer, W. E., & Stumptner, M. (2009). Modeling context-dependent faults for diagnosis. In F. Frisk, & E. Erik (Eds.), 20th International Workshop on Principles of Diagnosis (DX-09). Sweden: Linkoping University.
2009 Thiagarajan, R., Mayer, W., & Stumptner, M. (2009). Semantic service discovery by consistency-based matchmaking. In Advances In Data And Web Management, Proceedings Vol. 5446 (pp. 492-505). Germany: Springer.
DOI Scopus7 WoS5
2009 Thiagarajan, R. K., Mayer, W. E., & Stumptner, M. (2009). Generative composition of web services. In Y. Yu, & E. Eric (Eds.), CAiSE'09 Forum Proceedings Vol. 453 (pp. 49-54). Amsterdam, The Netherlands: CEUR.
2008 Mayer, W. E., Muehlenfeld, A., & Stumptner, M. (2008). Knowledge-intensive process modelling in engineering design. In Proceedings of the 19th international conference on database and expert systems applications (DEXA 2008) (pp. 90-94). US: IEEE Computer Society.
DOI
2008 Thiagarajan, R. K., Mayer, W. E., & Stumptner, M. (2008). Synthesis and partial execution of service proceses for matchmaking. In Proceedings of the second international workshop on service-orientated engineering and optimization (SENOPT 08) (pp. 25-33). Berlin, Heidelberg: Springer-Verlag.
2008 Schumann, A., Mayer, W. E., & Stumptner, M. (2008). Distributed repair of nondiagnosability. In Proceedings of the 18th european conference on artificial intelligence ,ECAI 2008. Frontiers in artificial intelligence and applications Vol. 178 (pp. 795-796). Amsterdam: IOS Press.
DOI
2008 Schumann, A., Mayer, W. E., & Stumptner, M. (2008). A jointree algorithm for diagnosability and its application to the verification of distributed software systems. In Proceedings of the 19th international workshop on principles of diagnosis ,DX-08 (pp. 165-172). Blue Mountains, NSW, Australia.
2008 Maier, F. T., Mayer, W. E., Stumptner, M., & Muehlenfeld, A. (2008). Ontology-based process modelling for design optimisation support. In Proceedings of the third international conference on design computing and cognition , DCC08 (pp. 513-532). Atlanta, Georgia, USA: Springer Science and Business Media B.V.
DOI Scopus9 WoS2
2008 Muehlenfeld, A., Mayer, W. E., Maier, F. T., & Stumptner, M. (2008). Ontology-based process modeling and execution using STEP/EXPRESS. In C. Cooke, & D. Daniel (Eds.), Proceedings of the 20th international conference on software engineering and knowledge engineering (pp. 935-940). USA: Knowledge Systems Institute Graduate School.
Scopus1
2008 Mayer, W. E., & Stumptner, M. (2008). Evaluating models for model-based debugging. In 23rd IEEE/ACM international conference on automated software engineering (ASE 2008) (pp. 128-137). USA: IEEE.
DOI Scopus60
2008 Muehlenfeld, A., Maier, F. T., Mayer, W. E., & Stumptner, M. (2008). Modelling and management of design artefacts in design optimisation. In Proceedings of the 15th ISPE international conference on concurrent engineering, CE2008. Collaborative product and service life cycle management for a sustainable world (pp. 513-520). UK: Springer.
DOI Scopus1 WoS1
2008 Mayer, W. E., Muehlenfeld, A., & Stumptner, M. (2008). Using ontologies to optimise design-driven development processes. In Proceedings of the 15th ISPE international conference on concurrent engineering CE2008. Collaborative product and sevice life cycle management for a sustainable world (pp. 451-459). London: Springer.
DOI
2008 Mayer, W. E., Abreu, R., Stumptner, M., & Gemund, A. (2008). Prioritising model-based debugging diagnostic reports. In Proceedings of the 19th international workshop on principles of diagnosis ,DX-08 (pp. 127-134). Blue Mountains, NSW, Australia.
2007 Mayer, W. E., & Stumptner, M. (2007). Models and tradeoffs in model-based debugging. In 18th International Workshop on Principles of Diagnosis (DX-07). Nashville, TN: Vanderbilt University.
2007 Thiagarajan, R. K., Stumptner, M., & Mayer, W. E. (2007). Semantic Web service composition by consistency-based model refinement. In Proceedings of the 2nd IEEE Asia-Pacific Service Computing Conference (pp. 336-343). USA: IEEE Computer Society.
DOI WoS1
2007 Mayer, W. E., & Stumptner, M. (2007). Abstract interpretation of programs for model-based debugging. In V. Veloso, & M. M (Eds.), Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (pp. 471-476). USA: IJCIA.
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2007 Thiagarajan, R., Stumptner, M., & Mayer, W. (2007). Semantic Web Service composition by consistency-based model refinement. In Proceedings of the 2nd IEEE Asia Pacific Services Computing Conference Apscc 2007 (pp. 336-343). IEEE.
DOI Scopus5
2006 Mayer, W. E., & Stumptner, M. (2006). Debugging failures in web services coordination. In SEKE 2006 : the 18th International Conference on Software Engineering and Knowledge Engineering : proceedings, technical program (pp. 536-543). Skokie, Il. US: Knowledge System Institute Graduate School.
2006 Mayer, W. E., & Stumptner, M. (2006). Better debugging through more abstract observations. In Proceedings of the 17th European Conference on Artificial Intelligence Vol. 141 (pp. 779-780). Amsterdam: IOS Press.
2005 Mayer, W. E., & Stumptner, M. (2005). Putting the Oracle in the driver's seat: debugging with arbitrary user input. In Working Papers of the 16th International Workshop on Principles of Diagnosis. Monterey, USA.
2005 Mayer, W. E., & Stumptner, M. (2005). Model-based debugging with high-level observations. In Intelligent Information Processing II Vol. 163 (pp. 299-309). USA: International Federation for Information Processing.
DOI
2004 Mayer, W. E., & Stumptner, M. (2004). High level observations in Java debugging. In ECAI 2004 : proceedings of the 16th European Conference on Artificial Intelligence Vol. 110 (pp. 1059-1060). Netherlands: IOS Press.
2004 Mayer, W. E., & Stumptner, M. (2004). Approximate modeling for debugging of program loops. In DX'04 : 15th International Workshop on Principles of Diagnosis. Carcassonne, France: LAAS.
2004 Mayer, W. E., & Stumptner, M. (2004). Debugging program loops using approximate modeling. In ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings Vol. 110 (pp. 843-847). Netherlands: IOS Press.
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2003 Mayer, W. E., & Stumptner, M. (2003). Extending diagnosis to debug programs with exceptions. In Proceedings of the 18th IEEE international conference on automated software engineering (pp. 240-244). Los Alamitos, CA, USA: IEEE Computer Society.
DOI Scopus17 WoS10
2003 Mayer, W. E., & Stumptner, M. (2003). Model-based debugging using multiple abstract models. In Proceedings of the Fifth International Workshop on Automated Debugging. http://arxiv.org/PS_cache/cs/pdf/0309/0309030.pdf: Computing Research Repository.
2003 Mayer, W. E., Stumptner, M., & Wotawa, F. (2003). Debugging program exceptions. In Proceedings of the Fourteenth International Workshop on Principles of Diagnosis. http://monet.aber.ac.uk:8080/monet/members/dx03_proceedings.html: DX-03.
2002 Wotawa, F., Stumptner, M., & Mayer, W. (2002). Model-based debugging or how to diagnose programs automatically. In Developments in Applied Artificial Intelligence (pp. 746). Germany: Springer-Verlag.
DOI
2002 Mayer, W. E., Stumptner, M., Wieland, D., & Wotawa, F. (2002). Can AI help to improve debugging substantially? Debugging experiences with value-based models. In ECAI 2002: 15th European Conference on Artificial Intelligence, July 21-26 2002, Lyon, France including Prestigious Applications of Intelligent Systems (PAIS 2002) : proceedings Vol. 77 (pp. 417-421). The Netherlands: IOS Press.
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2002 Mayer, W. E., Stumptner, M., Wieland, D., & Wotawa, F. (2002). Towards an integrated debugging environment. In ECAI 2002 Vol. 77 (pp. 422-426). The Netherlands: IOS Press.
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2002 Mayer, W. E., & Stumptner, M. (2002). Modeling programs with unstructured control flow for debugging. In Lecture notes in computer science Vol. 2557 (pp. 107-118). Berlin, Germany: Springer-Verlag.
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2002 Mayer, W., & Stumptner, M. (2002). Modeling programs with unstructured control flow for debugging. In Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science Vol. 2557 (pp. 107-118). Springer Berlin Heidelberg.
DOI Scopus5

  • Enhancing Asset Reliability and Risk Interoperability for Scale and Sparse Reliability Data, Future Energy Exports CRC Limited, 06/10/2025 - 17/10/2027

  • FEnEx CRC Project Zero - 20.RP0.001, Future Energy Exports CRC Limited, 01/12/2020 - 31/12/2026

  • SENTRI – Sensor-based Environmental sense-making Network for Threat Response and Information, Defence Innovation Partnership, 12/02/2025 - 02/09/2026

  • Advancing a digital tool for improved water decision-making in agriculture, Australia's Economic Accelerator Ignite Grant, 15/08/2025 - 14/08/2026

  • FEnEx 20.RP3.0048: Open Specification for Analytics Interoperability, Future Energy Exports CRC Limited, 01/04/2021 - 30/06/2026

  • FEnEX 21.RP3.0106:  Asset Reliability and Risk Interoperability to Optimise Maintenance Execution and Improve Risk Management of Energy Producers, Future Energy Exports CRC Limited, 01/07/2022 - 01/01/2026

  • Enhancing the RAN’s Undersea Surveillance Minimum Viable Capability, Defence SA, 14/03/2024 - 01/01/2026

  • DHCRC-0156: A Predictive Harm Response Management algorithmic tool to reduce adverse events in healthcare setting, Digital Health CRC, 20/07/2021 - 31/12/2025

  • Narrative Visualisation for Multi-Domain Agile C2, Defence Science and Technology Group, 20/12/2023 - 31/12/2025

  • Int Policing: Entity Linking & Resolution, D2D CRC Limited, 01/01/2016 - 31/12/2019

  • CERA 329 DeepRay Glider Robust Autonomy, Cwth Dept of Defence, 24/03/2017 - 31/05/2019

  • Urban Best Practice Expert System, UN-Habitat, 16/05/2018 - 16/05/2019

  • Modelling Complex Warfighting Strategic Response Initiative, SR2, Defence Science and Technology Group, 09/04/2018 - 30/06/2018

  • Techniques of Generating Behavioral Models for Constructive Combat Simulations, Cwth Dept of Defence, 01/09/2016 - 30/05/2017

Courses I teach

  • COMP 3024 System Architecture (2025)
  • INFT 5021 Capstone Professional Project (2025)
  • COMP 3024 System Architecture (2024)
  • INFS 2044 System Design and Realisation (2024)

Date Role Research Topic Program Degree Type Student Load Student Name
2025 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Kevin Hickson
2025 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Nilanjana Sarker
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Naeem Paeedeh
2023 Principal Supervisor - Doctor of Philosophy Doctorate Full Time Ifrah Siddiqui
2023 Principal Supervisor - Doctor of Philosophy Doctorate Full Time Mr Cong-Linh Le
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Ms Ranjita Sapkota
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Gourav Gupta
2022 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Mahesh Wickramarachchi Wickramaarachchige
2022 Principal Supervisor - - Master Full Time Mr Benjamin Babu
2022 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Seunglim Lee
2022 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Kevin Coburn
2022 Principal Supervisor - Doctor of Philosophy Doctorate Full Time Ms Shathika Koralagamage
2022 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Umar Memon
2022 Principal Supervisor - Doctor of Philosophy Doctorate Full Time Mr Kyle Ferdinand Hartzenberg
2022 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Ms Shaila Maheshwari
2021 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Chris Michael Boyd
2021 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Keith Thomas Man
2019 Principal Supervisor - Doctor of Philosophy Doctorate Full Time Mr Tom John Clarke

Date Role Editorial Board Name Institution Country
2025 - ongoing Associate Editor Data Mining and Machine Learning Scilight Australia
2020 - ongoing Associate Editor International Journal of Modeling and Simulation Taylor & Francis United Kingdom

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