International audience

Mobility datasets are crucial for various applications. However, sharing this data raises privacy concerns due to the sensitive nature of geolocation information. Synthetic data generation has recently emerged as a promising solution to protect geo-privacy of trajectory data. Current approaches rely on having a large set of authentic trajectories collected from individual users to train generative networks. However, this assumption proves impractical in many real-world scenarios due to the sensitive personal information typically embedded within trajectories. Our approach leverages federated learning to generate privacy-preserving synthetic trajectories without the need for centralized data collection. Experimental results demonstrate that our distributed framework effectively produces synthetic trajectories with distributions comparable to baseline, offering a privacy-conscious alternative for geo-privacy protection in mobility datasets.

Process mining techniques allow organizations to discover, monitor and improve their as-is processes by analyzing the process execution data, aka event data, recorded by their information systems. A recurrent task in process mining is querying. Querying allows users to get insights into specific executions of their processes and to retrieve relevant data. Existing process querying techniques require end users to be knowledgeable of the query language and the database schema. However, a key success factor for process analysis is to make querying accessible to business experts who may be inexperienced in database querying. This paper addresses this challenge by proposing a natural language interface (NLI) for querying event data. The interface allows users to formulate their questions in natural language and to automatically translate the questions into a structured query that can be executed over a database. We use graph based storage techniques, namely labeled property graphs, which allow to explicitly model event data relationships. As an executable query language, we use the Cypher language which is widely used for querying property graphs. The approach has been implemented and evaluated using a publicly available event log.