Semantic-aware building and summarization of multiple aspect trajectories

The proliferation of motion sensors has signi?cantly contributed to the availability of mobility data. An important line of research focuses on augmenting these datasets with diverse semantic information, referred to as aspects, thereby yielding multiple aspect trajectories (MATs). However, a notable gap in the existing literature pertains to the absence of methodologies for obtaining MATs and the scarcity of real-world datasets. To address this gap, we introduce MAT?B??????, an innovative system designed to facilitate the customization of semantic enrichment of trajectories through the use of arbitrary aspects and external data sources. Notably, the richness of information endowed by MAT?B?????? may introduce challenges in terms of data management and storage. Consequently, we propose MAT?S??, an approach tailored to summarize trajectories while preserving their semantic information.