Publications

This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS tracks of vehicles and vessels using temporal graph neural networks (GNNs) and counterfactuals.

This publication presents an ontology-based framework designed to address the complexities of international data transfers and ensure compliance with the General Data Protection Regulation (GDPR) and related regulations.

This publication presents an ontology-based framework designed to address the complexities of international data transfers and ensure compliance with the General Data Protection Regulation (GDPR) and related regulations.

Privacy preservation over federated data has gained its momentum in the era of securing users’ sensitive information. Combining and analysing sensitive information from multiple data sources offers considerable potential for knowledge discovery.

Although much work has been done on explainability in the computer vision and natural language processing (NLP) fields, there is still much work to be done to explain methods applied to time series as time series by nature can not be understood at first sight.

Outliers can affect trajectory analysis as they represent errors. There are two outlier detection categories, one focusing on a collection of trajectories, where one whole trajectory can be an outlier and another on points inside individual trajectories. In this paper, we focus on the latter.

Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data.