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

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 65–72 (2022)

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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.

References

1. P. Kucharski, D. Sielski, T. Jaworski, A. Romanowski, and J. Kucharski, “Use of fuzzy cognitive maps for enhanced interaction with multiple mobile devices.” in FedCSIS (Communication Papers), 2018, pp. 217–222.
2. V. Bogorny, C. Renso, A. R. de Aquino, F. de Lucca Siqueira, and L. O. Alvares, “Constant–a conceptual data model for semantic trajectories of moving objects,” Transactions in GIS, vol. 18, no. 1, pp. 66–88, 2014.
3. R. d. S. Mello, V. Bogorny, L. O. Alvares, L. H. Z. Santana, C. A. Ferrero, A. A. Frozza, G. A. Schreiner, and C. Renso, “Master: A multiple aspect view on trajectories,” Transactions in GIS, vol. 23, no. 4, pp. 805–822, 2019.
4. K. S. Hornsby and S. Cole, “Modeling moving geospatial objects from an event-based perspective,” Transactions in GIS, vol. 11, no. 4, pp. 555–573, 2007.
5. S. Spaccapietra, C. Parent, M. L. Damiani, J. A. de Macedo, F. Porto, and C. Vangenot, “A conceptual view on trajectories,” Data & knowledge engineering, vol. 65, no. 1, pp. 126–146, 2008.
6. R. Fileto, C. May, C. Renso, N. Pelekis, D. Klein, and Y. Theodoridis, “The baquara2 knowledge-based framework for semantic enrichment and analysis of movement data,” Data & Knowledge Engineering, vol. 98, pp. 104–122, 2015.
7. C. A. Ferrero, L. O. Alvares, and V. Bogorny, “Multiple aspect trajectory data analysis: research challenges and opportunities.” in GeoInfo, 2016, pp. 56–67.
8. T. T. Portela, F. Vicenzi, and V. Bogorny, “Trajectory data privacy: Research challenges and opportunities.” in GEOINFO, 2019, pp. 99–110.
9. W. Pedrycz, A. Skowron, and V. Kreinovich, Handbook of granular computing. John Wiley & Sons, 2008.
10. L. A. Zadeh, “Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic,” Fuzzy sets and systems, vol. 90, no. 2, pp. 111–127, 1997.
11. W. Pedrycz, Granular computing: analysis and design of intelligent systems. CRC press, 2018.
12. G. Wang, Q. Liu, Y. Yao, and A. Skowron, “Rough sets, fuzzy sets, data mining, and granular computing,” RSFDGrC, Springer 2003, Chongqing, China, pp. 41–48, 2003.
13. Z. Pawlak, “Granularity of knowledge, indiscernibility and rough sets,” in 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98CH36228), vol. 1. IEEE, 1998, pp. 106–110.
14. D. Dubois and H. Prade, “Bridging gaps between several forms of granular computing,” Granular Computing, vol. 1, no. 2, pp. 115–126, 2016.
15. Y. Yao, “A triarchic theory of granular computing,” Granular Computing, vol. 1, no. 2, pp. 145–157, 2016.
16. W. Pedrycz and W. Homenda, “Building the fundamentals of granular computing: A principle of justifiable granularity,” Applied Soft Computing, vol. 13, no. 10, pp. 4209–4218, 2013.
17. W. Pedrycz, A. Gacek, and X. Wang, “Clustering in augmented space of granular constraints: a study in knowledge-based clustering,” Pattern Recognition Letters, vol. 67, pp. 122–129, 2015.
18. L. Livi and A. Sadeghian, “Data granulation by the principles of uncertainty,” Pattern Recognition Letters, vol. 67, pp. 113–121, 2015.
19. ——, “Granular computing, computational intelligence, and the analysis of non-geometric input spaces,” Granular Computing, vol. 1, no. 1, pp. 13–20, 2016.
20. G. D’Aniello, A. Gaeta, V. Loia, and F. Orciuoli, “A granular computing framework for approximate reasoning in situation awareness,” Granular Computing, vol. 2, no. 3, pp. 141–158, 2017.
21. K. Zhou, Z. Tian, and Y. Yang, “Periodic pattern detection algorithms for personal trajectory data based on spatiotemporal multi-granularity,” IEEE Access, vol. 7, pp. 99 683–99 693, 2019.
22. F. J. Cabrerizo, J. A. Morente-Molinera, S. Alonso, W. Pedrycz, and E. Herrera-Viedma, “Improving consensus in group decision making with intuitionistic reciprocal preference relations: A granular computing approach,” in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018, pp. 1471–1476.
23. L. A. Zadeh, “Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems,” Soft computing, vol. 2, no. 1, pp. 23–25, 1998.
24. C. Yunxian, L. Renjie, Z. Shuliang, and G. Fenghua, “Correction: Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing,” PloS one, vol. 15, no. 5, p. e0233068, 2020.
25. M. Antonelli, P. Ducange, B. Lazzerini, and F. Marcelloni, “Multiobjective evolutionary design of granular rule-based classifiers,” Granular Computing, vol. 1, no. 1, pp. 37–58, 2016.
26. W. Pedrycz, “Evolvable fuzzy systems: some insights and challenges,” Evolving Systems, vol. 1, no. 2, pp. 73–82, 2010.
27. C. Silvestri and A. Vaisman, Mobility and Uncertainty. Cambridge University Press, 2013, p. 83–102.
28. W. Xu, J. Mi, and W. Wu, “Granular computing methods and applications based on inclusion degree,” Beijing: Science Press, 2017.
29. M. Bishop, J. Cummins, S. Peisert, A. Singh, B. Bhumiratana, D. Agarwal, D. Frincke, and M. Hogarth, “Relationships and data sanitization: A study in scarlet,” in Proceedings of the 2010 New Security Paradigms Workshop, 2010, pp. 151–164.
30. C. Wafo Soh, L. Njilla, K. Kwiat, and C. Kamhoua, “Learning quasi-identifiers for privacy-preserving exchanges: A rough set theory approach,” Granular Computing, vol. 5, no. 1, pp. 71–84, 2020.