Abstract
While the problems of finding the shortest path and k-shortest paths have been extensively researched, the research community has been shifting its focus towards discovering and identifying paths based on user preferences. Since users naturally follow some of the paths more than other paths, the popularity of a given path often reflects such user preferences. Moreover, users typically prefer diverse paths over similar paths for gaining flexibility in path selection. Given a set of user traversals in a road network and a set of paths between a given source and destination pair, we propose a scheme based on transactional modeling and pattern mining for performing top-k ranking of these paths based on both path popularity and path diversity. Our performance evaluation with a real dataset demonstrates the effectiveness of the proposed scheme.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggarwal, C.C., Han, J. (eds.): Frequent Pattern Mining. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07821-2
Chang, K.P., Wei, L.Y., Yeh, M.Y., Peng, W.C.: Discovering personalized routes from trajectories. In: Proceedings of ACM SIGSPATIAL, pp. 33–40 (2011)
Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: Proceedings of ICDE, pp. 900–911 (2011)
Chondrogiannis, T., Bouros, P., Gamper, J., Leser, U.: Alternative routing: k-shortest paths with limited overlap. In: Proceedings of ACM SIGSPATIAL (2015)
Chondrogiannis, T., Bouros, P., Gamper, J., Leser, U.: Exact and approximate algorithms for finding k-shortest paths with limited overlap. In: Proceedings of EDBT 2017, pp. 414–425 (2017)
Chondrogiannis, T., Gamper, J.: ParDiSP: a partition-based framework for distance and shortest path queries on road networks. In: Proceedings of MDM, vol. 1, pp. 242–251 (2016)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)
Feng, Z., Zhu, Y.: A survey on trajectory data mining: techniques and applications. IEEE Access 4, 2056–2067 (2016)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29, 1–12 (2000)
Hershberger, J., Maxel, M., Suri, S.: Finding the k shortest simple paths: a new algorithm and its implementation. ACM Trans. Algorithms 3(4), 45 (2007)
Martins, E.Q., Pascoal, M.M.: A new implementation of Yen’s ranking loopless paths algorithm. Q. J. Belg. Fr. Ital. Oper. Res. Soc. 1(2), 121–133 (2003)
Sommer, C.: Shortest-path queries in static networks. ACM Comput. Surv. (CSUR) 46(4), 45 (2014)
Wei, L.Y., Chang, K.P., Peng, W.C.: Discovering pattern-aware routes from trajectories. Distrib. Parallel Databases 33(2), 201–226 (2015)
Wei, L.Y., Zheng, Y., Peng, W.C.: Constructing popular routes from uncertain trajectories. In: Proceedings of ACM SIGKDD, pp. 195–203 (2012)
Yen, J.Y.: Finding the k shortest loopless paths in a network. Manage. Sci. 17(11), 712–716 (1971)
Zheng, Y., Xie, X., Ma, W.Y.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Revanth Rathan, P., Krishna Reddy, P., Mondal, A. (2019). Discovering Diverse Popular Paths Using Transactional Modeling and Pattern Mining. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11706. Springer, Cham. https://doi.org/10.1007/978-3-030-27615-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-27615-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27614-0
Online ISBN: 978-3-030-27615-7
eBook Packages: Computer ScienceComputer Science (R0)