Fethi Rabhi

About

Fethi Rabhi is a Professor in the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Australia. His main research areas are in service-oriented software engineering with a strong focus on business and financial applications. He completed a PhD in Computer Science at the University of Sheffield in 1990 and held several academic appointments in the USA and the UK before joining UNSW in 2000. He is currently actively involved in several research projects in the area of large-scale news and financial market data analysis. He is a member of the Australian Computer Society (ACS) and IEEE Computer Society.

Employment

• UNSW Sydney Professor
  2000 - Present
• UNSW Sydney

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (302)

• Enterprise Information Exchange via IE-BSE Framework: A Case Study on E-invoicing
  2027 DOI: 10.1007/978-3-032-32026-1_39
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• Ecosystem-Centric Capstone: Lessons from Simulating a Tech Company in Software Engineering Education
  2026 DOI: 10.1145/3786580.3786973
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• AI-Driven Business Process Composition for Enterprise Information Exchange Within IE-BSE Framework: An Empirical Case Analysis on E-invoicing
  2026 DOI: 10.1007/978-3-032-26363-6_11
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• Complex Real-World Application Systems Behavioral Profiling Using a Dynamical Systems Phase Space Approach and Trending Techniques
  IEEE Access 2026 DOI: 10.1109/ACCESS.2026.3718144
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• Financial Trading Analytics Knowledge Management
  Communications in Computer and Information Science 2026 DOI: 10.1007/978-3-032-05727-3_35
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• CEDAR: An Ontology-Based Framework Using Event Abstractions to Contextualise Financial Data Processes
  Electronics 2025 DOI: 10.3390/electronics15010145
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• PipeMind: Toward a Multi-Agent Framework for Real-Time Feedback and Continuous Optimization in Analytics Pipelines
  2025 DOI: 10.1145/3701716.3715193
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• Benchmarking LLMs for Business Architecture Modelling with Hierarchical Capability Maps
  Lecture Notes in Computer Science 2025 DOI: 10.1007/978-3-031-94569-4_3
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• Editorial: Business transformation through AI-enabled technologies
  Frontiers in Artificial Intelligence 2025 DOI: 10.3389/frai.2025.1577540
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• Enhancing Analytics Pipelines with Advanced Multi-agent Feedback Integration
  Lecture Notes in Business Information Processing 2025 DOI: 10.1007/978-3-031-89933-1_4
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• Enhancing Collaborative Analytics with an Ontology: A Case Study with High Frequency Data Analysis
  Lecture Notes in Business Information Processing 2025 DOI: 10.1007/978-3-031-89933-1_3
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• GWise: A Graph-Structured Multi-Agent Framework for Service-Oriented and Generative-AI-Enabled Financial Trading Analytics
  Proceedings of the IEEE International Conference on Web Services Icws 2025 DOI: 10.1109/ICWS67624.2025.00136
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• Impact of Business Process Masking on Organizations’ Policies
  International Conference on Evaluation of Novel Approaches to Software Engineering Enase Proceedings 2025 DOI: 10.5220/0013170700003928
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• Intent Identification Using Few-Shot and Active Learning with User Feedback
  Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 2025 DOI: 10.1007/978-981-96-0573-6_4
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• Introduction to the Minitrack on Explainable Artificial Intelligence (XAI)
  Proceedings of the Annual Hawaii International Conference on System Sciences 2025
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• New Money: A Systematic Review of Synthetic Data Generation for Finance
  2025 DOI: 10.48550/arxiv.2510.26076
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• Truman: A Large Language Model-based Multi-agent Simulator for Synthetic Money Laundering Data Generation
  Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas 2025
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• Truman: A Large Language Model-based Multi-agent Simulator for Synthetic Money Laundering Data Generation
  Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas 2025
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• A machine learning-based universal outbreak risk prediction tool
  Computers in Biology and Medicine 2024 DOI: 10.1016/j.compbiomed.2023.107876
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• Addressing e-Invoicing Adoption Challenges using API Ecosystems
  ACIS 2024 Proceesings 2024
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