Publications

The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data.

In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts.

In the domain of Mobility Data Science, the intricate task of interpreting models trained on trajectory data, and elucidating the spatio-temporal movement of entities, has persistently posed significant challenges.

Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping.

MobiSpaces featured in the "ERCIM News 135" release (October 2023). ERCIM is published on a quarterly basis and reports on joint actions of the ERCIM partners, and aims to reflect the contribution made by ERCIM to the European Community in Information Technology and Applied Mathematics.

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.