The Community for Technology Leaders
RSS Icon
Issue No.05 - May (2013 vol.12)
pp: 945-956
Choonoh Lee , Korea Advanced Institute of Science and Technology
Giwan Yoon , Korea Advanced Institute of Science and Technology
Dongsoo Han , Korea Advanced Institute of Science and Technology
Research on place extraction has been of interest for the detection of meaningful places that users visit. Because interpretations of meaningful places may be different according to location-based applications, a universal place extraction algorithm that is able to detect all kinds of meaningful places needs to be developed. Unfortunately, most previously proposed place extraction algorithms failed to show high place detection accuracy and also failed to perfectly detect meaningful places. In this work, we propose a new place extraction algorithm that can significantly enhance the accuracy of place extraction. The basic concept of the proposed algorithm is a superstate model, which is an extension of the Hidden Markov Model (HMM); we substituted superstates for the simple probabilistic distributions of the HMM. Our proposed algorithm shows remarkable detection accuracy in place extraction, significantly higher than any other previously proposed algorithms. Furthermore, the proposed algorithm can efficiently operate in mobile environments because its computations are simple.
Clustering algorithms, Hidden Markov models, Global Positioning System, Accuracy, Algorithm design and analysis, Probabilistic logic, Vectors, place extraction, Location dependent and sensitive, pervasive computing
Choonoh Lee, Giwan Yoon, Dongsoo Han, "A Probabilistic Place Extraction Algorithm Based on a Superstate Model", IEEE Transactions on Mobile Computing, vol.12, no. 5, pp. 945-956, May 2013, doi:10.1109/TMC.2012.64
[1] Y.F. Tuan, Space and Place: The Perspective of Experience. Univ. of Minnesota, 1977.
[2] D. Ashbrook and T. Starner, “Using GPS to Learn Significant Locations and Predict Movement across Multiple Users,” Personal Ubiquitous Computing, vol. 7, no. 5, pp. 275-286, Oct. 2003, doi:10.1007/s00779-003-0240-0.
[3] C. Zhou, D. Frankowski, P. Ludford, S. Shekhar, and L. Terveen, “Discovering Personally Meaningful Places: An Interactive Clustering Approach,” ACM Trans. Information Systems, vol. 25, no. 3,article 12, , 2007.
[4] N. Toyama, T. Ota, F. Kato, Y. Toyota, T. Hattori, and T. Hagino, “Exploiting Multiple Radii to Learn Significant Locations,” Proc. First Int'l Workshop Location and Context Awareness (LoCA '05), pp. 157-168, May 2005, doi:10.1007/11426646_15.
[5] F. Schmid and K.F. Richter, “Extracting Places from Location Data Streams,” Proc. Second Int'l Workshop Ubiquitous Geographical Information Services (UbiGIS '06), Sept. 2006.
[6] A.T. Palma, V. Bogorny, B. Kuijpers, and L.O. Alvares, “A Clustering-Based Approach for Discovering Interesting Places in Trajectories,” Proc. ACM Symp. Applied Computing (SAC '08), pp. 863-868, Mar. 2008, doi:10.1145/1363686.1363886.
[7] M. Zimmermann, T. Kirste, and M. Spiliopoulou, “Finding Stops in Error-Prone Trajectories of Moving Objects with Time-Based Clustering,” Proc. Int'l Conf. Intelligent Interactive Assistance and Mobile Multimedia Computing (IMC '09), vol. 53, pp. 275-286, Nov. 2009, doi:10.1007/978-3-642-10263-9_24.
[8] X. Cao, G. Cong, and C.S. Jensen, “Mining Significant Semantic Locations from GPS Data,” Proc. VLDB Endowment, vol. 3, nos. 1/2, pp. 1009-1020, Sept. 2010.
[9] J. Kang, W. Welbourne, B. Stewart, and G. Borriello, “Extracting Places from Traces of Locations,” Proc. Second ACM Int'l Workshop Wireless Mobile Applications and Services on WLAN Hotspots, pp. 110-118, Sept./Oct. 2004, doi:10.1145/1024733.1024748.
[10] R. Hariharan and K. Toyama, “Project Lachesis: Parsing and Modeling Location Histories,” Proc. Int'l Conf. Geographic Information Science (GIScience '04), pp. 106-124, Oct. 2004.
[11] D.H. Hu and C.L. Wang, “GPS-Based Location Extraction and Presence Management for Mobile Instant Messenger,” Proc. Int'l Conf. Embedded and Ubiquitous Computing (EUC '07), pp. 309-320, Dec. 2007, doi:10.1007/978-3-540-77092-3_27.
[12] Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W.Y. Ma, “Mining User Similarity Based on Location History,” Proc. 16th ACM SIGSPATIAL Int'l Conf. Advances Geography Information Systems (GIS '08), pp. 34:1-34:10, Nov. 2008, doi:10.1145/1463434.1463477.
[13] Y. Ye, Y. Zheng, Y. Chen, J. Feng, and X. Xie, “Mining Individual Life Pattern Based on Location History,” Proc. 10th Int'l Conf. Mobile Data Management: Systems Services and Middleware, pp. 1-10, May 2009, doi:10.1109/MDM.2009.11.
[14] R. Montoliu and D. Gatica-Perez, “Discovering Human Places of Interest from Multimodal Mobile Phone Data,” Proc. Ninth Int'l Conf. Mobile and Ubiquitous Multimedia (MUM '10), pp. 12:1-12:10, Dec. 2010, doi:10.1145/1899475.1899487.
[15] Many-Worlds Interpretation, Wikipedia, , 2012.
[16] D. Deutsch, The Fabric of Reality. Penguin Books, 1997.
[17] J. Hightower, S. Consolvo, A. LaMarca, I. Smith, and J. Hughes, “Learning and Recognizing the Places We Go,” Proc. Seventh Int'l Conf. Ubiquitous Computing (Ubicomp '05), pp. 159-176, Sept. 2005, doi:10.1007/11551201_10.
[18] D.H. Kim, J. Hightower, R. Govindan, and D. Estrin, “Discovering Semantically Meaningful Places from Pervasive RF-Beacons,” Proc. 11th Int'l Conf. Ubiquitous Computing (Ubicomp '09), pp. 21-30, Sept./Oct. 2009, doi:10.1145/1620545.1620549.
[19] D.H. Kim, Y. Kim, D. Estrin, and M.B. Srivastava, “SensLoc: Sensing Everyday Places and Paths Using Less Energy,” Proc. Eighth ACM Conf. Embedded Networked Sensor Systems, pp. 43-56, Nov. 2010, doi: 10.1145/1869983.1869989.
[20] K. Laasonen, M. Raento, and H. Toivonen, “Adaptive on-Device Location Recognition,” Proc. Second Int'l Conf. Pervasive Computing, pp. 287-304, Apr. 2004, doi:10.1007/978-3-540-24646-6_21.
[21] G. Yang, “Discovering Significant Places from Mobile Phones - A Mass Market Solution,” Proc. Second Int'l Conf. Mobile Entity Localization and Tracking in GPS-less Environment (MELT '09), pp. 34-49, Sept. 2009.
[22] S. Isaacman, R. Becker, R. Càceres, S. Kobourov, M. Martonosi, J. Rowland, and A. Varshavsky, “Identifying Important Places in People's Lives from Cellular Network Data,” Proc. Ninth Int'l Conf. Pervasive Computing (Pervasive '11), vol. 6696, pp. 133-151, June 2011, doi:10.1007/978-3-642-21726-5_9.
[23] M. Ester, H.P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proc. Second ACM Int'l Conf. Knowledge Discovery and Data Mining (KDD '96), pp. 226-241, 1996.
[24] M. Ankerst, M.M. Breunig, H.P. Kriegel, and J. Sander, “OPTICS: Ordering Points to Identify the Clustering Structure,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '99), pp. 49-60, June 1999, doi:10.1145/304182.304187.
[25] L. Liao, D. Fox, and H. Kautz, “Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields,” Int'l J. Robotic Research, vol. 26, no. 1, pp. 119-134, Jan. 2007. doi:10.1177/0278364907073775.
[26] K. Zhang, H. Li, K. Torkkola, and M. Gardner, “Adaptive Learning of Semantic Locations and Routes,” Proc. First Int'l Conf. Autonomic Computing Comm. Systems, pp. 3:1-3:10, Oct. 2007.
[27] P. Nurmi and S. Bhattacharya, “Identifying Meaningful Places: The Non-Parametric Way,” Proc. Sixth Int'l Conf. Pervasive Computing (Pervasive '08), pp. 111-127, May 2008, doi:10.1007/978-3-540-79576-6_7.
[28] P. Nurmi, “Identifying Meaningful Places,” PhD Thesis, Dept. of Computer Science, Univ. of Helsinki, Finland, http://urn.fiURN:ISBN:978-952-10-5790-8, 2009.
[29] Google Nexus One, Wikipedia,, 2012.
[30] Superstate Model,, 2012.
64 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool