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Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 32

Towards a Granular Computing Framework for Multiple Aspect Trajectory Representation and Privacy Preservation: Research Challenges and Opportunities


DOI: http://dx.doi.org/10.15439/2022F282

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 6572 ()

Full text

Abstract. In recent years, there has been a lot of research on trajectory data analysis and mining. Despite the fact that trajectories are multidimensional data characterized by the spatial, the temporal, as well as the semantic aspect, few are the studies that have taken into account all of these three dimensions. Apart from these dimensions that should be considered all together for an efficient trajectory data analysis and mining, it should be also made feasible to represent trajectories from several points of view, which is called multiple aspect representation. State-of-the-art works are typically restricted to a single trajectory representation, which limits the identification of a variety of key patterns. These multiple aspect trajectories are quite rich that they reveal sensitive information, making the user's privacy vulnerable; hence, raising several challenges when it comes to privacy preservation. In this paper, we show that there is a need to consider granular computing for multiple aspect trajectory representation and privacy preservation, and present new research challenges and opportunities in this concern.


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