1st Workshop on Generation of Synthetic Datasets for Information Systems (GenSyn)

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Workshops Details
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In the age of advanced information systems, data is crucial for deploying and evaluating systems, but collecting usable benchmarks is challenging due to privacy, scarcity, and legal concerns. Synthetic datasets have become valuable for machine learning tasks, supporting data-driven applications, AI, IoT, Digital Twins, and business process models. The GenSyn workshop aims to discuss generative AI and classical techniques for creating synthetic datasets for AI and non-AI information systems, presenting state-of-the-art tools and approaches.

Our workshop is a dedicated forum to encourage the exploration of how synthetic datasets can be integrated across diverse information system engineering (ISE) contexts, which is a developing field and a promising application area for AI, where many approaches are not mature enough yet for publication at the main track and are more exploratory.
Areas of Interest include, but are not limited to:

- Synthetic data for AI and Machine Learning in ISE: Generating high-quality synthetic data to support machine learning applications within ISE, including deep learning, natural language processing, and generative AI models;

- Method, techniques, and algorithms to generate synthetic data for IS, spanning from AI/ML model to traditional frameworks, e.g., model-based frameworks;

- Process Automation and Mining with Synthetic Data: Utilizing synthetic datasets to improve data-driven applications, process mining, and business process modeling and simulation;

- Synthetic data generation to support data management systems, e.g., DBMS and knowledge graphs;

- Synthetic datasets for real-time application: Creating synthetic datasets to conceive digital twins, simulate real-time data streams in IoT systems, and support advanced driver assistance systems (ADAS);

- Data-driven compliance and governance: Addressing regulatory and privacy concerns through synthetic data generation to support decision-making and compliance in sensitive systems, e.g., eGovernment and healthcare;

- Evaluation of Synthetic Data in Real-world Contexts: Developing benchmarks and methodologies to validate the quality, diversity, sustainability, and realism of synthetic datasets in business intelligence and industry-specific systems;

- Case studies and experience reports from academia and industry

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Important Dates
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- Full paper submission: March 7th, 2025
- Notification: April 7th, 2025
- Camera-ready: April 14th, 2025

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Organizers
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Claudio Di Sipio (University of L’Aquila, Italy)
Arianna Fedeli (Gran Sasso Science Institute, Italy)
Eduard Kamburjan (IT University of Copenaghen, Denmark)
Riccardo Rubei (University of L’Aquila, Italy)