Outlier detection and cleaning are essential steps in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on outlier points within individual trajectories, i.e., points that deviate significantly inside a single trajectory.
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously.
The increasing prevalence of mobility data in diverse applications such as traffic management requires specialized tools for manipulating it. This paper introduces MEOS (Mobility Engine Open Source), a versatile C library designed explicitly for managing and processing mobility data.
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.
Automated Machine Learning (AutoML) aims to identify the best-performing machine learning algorithm along with its input parameters for a given data set and a speciic machine learning task.
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.
