Abstract
The existing semantic enrichment process approaches which can produce semantic trajectories, are generally time consuming. In this paper, we propose a semantic enrichment process framework for spatiotemporal trajectories in geospatial environment. It can derive new semantic trajectories through the three phases: pre-annotated semantic trajectories storage, spatiotemporal similarity measurement, and semantic information matching. Having observed the common trajectories in the same geospatial object scenes, we propose an algorithm to match semantic information in pre-annotated semantic trajectories to new spatiotemporal trajectories. Finally, we demonstrate the effectiveness and efficiency of our proposed approach by using the real dataset.
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
Similar content being viewed by others
References
Zhixian Y.: Towards semantic trajectory data analysis: a conceptual and computational approach. In: VLDB 2009, Lyon, France (2009)
Zhixian, Y., Dipanjan, C., Christine, P., Stefano, S., Karl, A.: Semantic trajectories: mobility data computation and annotation. ACM TIST 4(3), 1–38 (2013)
Christine, P., et al.: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4), 1–32 (2013)
Daniel, A., Thad, S.: Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiquit. Comput. 7(5), 275–286 (2003). https://doi.org/10.1007/s00779-003-0240-0
Krumm, J., Horvitz, E.: Predestination: inferring destinations from partial trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006). https://doi.org/10.1007/11853565_15
Andrey, T., Vania, B., Bart, K., Luis O.: A clustering-based approach for discovering interesting places in trajectories. In: SAC 2008, pp. 863–868, Fortaleza, Ceara, Brazil (2008)
Yu, Z., Lizhu, Z., Zhengxin, M., Xing, X., Wei-Ying, M.: Recommending friends and locations based on individual location history. TWEB 5(1), 1–44 (2011)
Stefano, S., Christine, P., Maria, L., Jose, A., Fabio, P., Christelle, V.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)
Miriam, B., Jose, A., Chiara, R., Roberto, T., Monica, W.: Towards semantic interpretation of movement behavior. In: AGILE 2009, pp. 271–288. Hannover, Germany (2009)
Nogueira, T.P., Martin, H.: Qualitative representation of dynamic attributes of trajectories. In: Agile Conference on Geographic Information Science (2014)
Tales, P., Reinaldo, B., Carina, T., Herve, M., Fabio, P., Christelle, V.: Framestep: a framework for annotating semantic trajectories based on episodes. Expert Syst. Appl. 92, 533–545 (2018)
Longgang, X., Tao, W., Jianya, G.: A geo-spatial information oriented trajectory model and spatio-temporal pattern quering. Acta Geodactica et Catographica Sin. 43(9), 982–988 (2014)
Kima, J., Mahmassanibhan, S.: Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories. In: 21st International Symposium on Transportation and Traffic Theory, pp. 164–184 (2015)
Acknowledgements
This work is supported by the National Natural Science Foundation of China No. 41971343 and NSFC.61702271.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Han, J., Liu, M., Ji, G., Zhao, B., Liu, R., Li, Y. (2020). Efficient Semantic Enrichment Process for Spatiotemporal Trajectories in Geospatial Environment. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-60290-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60289-5
Online ISBN: 978-3-030-60290-1
eBook Packages: Computer ScienceComputer Science (R0)