Abstract
Mobile devices have access to multiple sources of location data, but at any particular time often only a fraction of the location information sources is available. Fusion of location information can provide reliable real-time location awareness on the mobile phone. In this paper we propose and evaluate a novel approach to detecting the places of interest based on density-based clustering. We address both extracting the information about relevant places from the combined location information, and detecting the visits to known places in the real time. In this paper we also propose and evaluate ContReMAR application – an application for mobile context and location awareness. We use Nokia MDC dataset to evaluate our findings, find the proper configuration of clustering algorithm and refine various aspects of place detection.
For the color version of the figures, please refer to the online version of the paper.
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
Preview
Unable to display preview. Download preview PDF.
References
Alvares, L.O., Bogorny, V., Kuijpers, B., Macedo, J., Moelans, B., Vaisman, A.: A Model for Enriching Trajectories with Semantic Geographical Information. In: GIS, p. 22 (2007)
Andrienko, G., Andrienko, N., Hurter, C., Rinzivillo, S., Wrobel, S.: From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data. In: IEEE Visual Analytics Science and Technology (VAST 2011) Proceedings, pp. 161–170. IEEE Computer Society Press (2011)
Ankerst, M., Breunig, M.M., Kriegel, H., Sander, J.: OPTICS: Ordering Points To Identify the Clustering Structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data (SIGMOD 1999), pp. 49–60. ACM, New York (1999)
Boytsov, A., Zaslavsky, A.: ECSTRA – Distributed Context Reasoning Framework for Pervasive Computing Systems. In: Balandin, S., Koucheryavy, Y., Hu, H. (eds.) NEW2AN/ruSMART 2011. LNCS, vol. 6869, pp. 1–13. Springer, Heidelberg (2011)
Boytsov, A., Zaslavsky, A.: Formal verification of context and situation models in pervasive computing. Pervasive and Mobile Computing (available online March 23, 2012), ISSN 1574-1192, doi:10.1016/j.pmcj.2012.03.001
Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. KDD, vol. 96, pp. 226–231. AAAI Press (1996) ISBN: 1577350049
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Han, J., Lee, J.-G., Gonzalez, H., Li, X.: Mining Massive RFID, Trajectory, and Traffic Data Sets (Tutorial). In: KDD (2008)
Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of Convoys in Trajectory Databases. In: VLDB, pp. 1068–1080 (2008)
Laurila, J.K., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O., Do, T.-M.-T., Dousse, O., Eberle, J., Miettinen, M.: The Mobile Data Challenge: Big Data for Mobile Computing Research. In: Proc. Mobile Data Challenge by Nokia Workshop, in Conjunction with Int. Conf. on Pervasive Computing, Newcastle (June 2012)
Li, Z., Ji, M., Lee, J.-G., Tang, L.-A., Yu, Y., Han, J., Kays, R.: MoveMine: Mining Moving Object Databases. In: SIGMOD, pp. 1203–1206 (2010)
Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A Clustering-based Approach for Discovering Interesting Places in Trajectories. In: 23rd Annual Symposium on Applied Computing (ACM-SAC 2008), Fortaleza, Ceara, Brazil, March 16-20, pp. 863–868 (2008)
Spaccapietra, S., Parent, C., Damiani, M.L., de Macedo, J.A., Porto, F., Vangenot, C.: A Conceptual View on Trajectories. Data and Knowledge Engineering 65, 126–146 (2008)
Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: SeMiTri: A Framework for Semantic Annotation of Heterogeneous Trajectories. In: 14th International Conference on Extending Database Technology (EDBT 2011), pp. 259–270 (2011)
Google Places API documentation, https://developers.google.com/maps/documentation/places/ (accessed on April 14, 2012)
Elvin Protocol Specifications, http://www.elvin.org/specs/index.html (accessed on April 14, 2012)
Avis Event Router Documentation, http://avis.sourceforge.net/documentation.html (accessed on April 14, 2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boytsov, A., Zaslavsky, A., Abdallah, Z. (2012). Where Have You Been? Using Location Clustering and Context Awareness to Understand Places of Interest. In: Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networking. ruSMART NEW2AN 2012 2012. Lecture Notes in Computer Science, vol 7469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32686-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-32686-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32685-1
Online ISBN: 978-3-642-32686-8
eBook Packages: Computer ScienceComputer Science (R0)