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3D Continuous K-NN Query for a Landmark-based Wayfinding Location-based Service

  • Najmeh Samany
  • Mohmoud Reza Delavar
  • Sara Saeedi
  • Reza Aghataher
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Wayfinding in unfamiliar indoor and outdoor environments are particularly intricate problems of all the people’s activities. The development of assistive technologies to aid wayfinding is hampered by the lack of reliable and efficient methods providing location information in the environment. Location-based services (LBS) are systems which support wayfinding task as process. They do not support landmark-based wayfinding although researchers agreed on its efficient role. In addition, one of the challenges of LBS is the continuous query which guarantees the performance of the system. In this paper, we propose an efficient method for 3D continuous k-NN query. Our method is based on three ideas, (1) The dynamic mobile objects have 3D coordinate space (x,y,t) and static landmark with 2D spaces (x,y) (2) selecting landmarks which are the nearest neighbor of one or more continuous queries, (3) indexing the queries rather than the landmarks. Through some experimental evaluation we demonstrate that our method is applicable on increasing runtime and decreasing power consumption of mobile devices.

Keywords

Index Structure Range Query Decision Point Mobile Client Split Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Allen, G.: Spatial abilities, cognitive maps, and wayfinding - bases for individual differences in spatial cognition and behavior. In: Golledge, R. (Ed.), Wayfinding Behavior Cognitive Mapping and Other Spatial Processes, Johns Hopkins University Press, Baltimore, pp. 46—80 (1999).Google Scholar
  2. Baus, J. Krger, A., Wahlster, W.: A Resource-Adaptive Mobile Navigation System, International Conference on Intelligent User Interfaces IUI02, January 13-16, 2002, San Francisco. http://citeseer.ist.psu.edu/baus02resourceadaptive.html (2002).Google Scholar
  3. Brenner, C. and Elias, B.: Extracting Landmarks for Car Navigation Systems using existing GIS databases and laser scanning. Proceeding ISPRS Workshop on Photogrammetric Image Analysis, Munchen, Germany, (2003).Google Scholar
  4. Caduff, D., Timpf, S.: The Landmark Spider: Representing Landmark Knowledge for Wayfinding Tasks. In: Barkowsky T, Freksa C, Hegarty M, Lowe R (eds) Reasoning with Mental and External Diagrams: Computational Modeling and Spatial Assistance, AAAI Press, Stanford, CA USA, pp 30—35 (2005).Google Scholar
  5. Cai, Y., Hua, K. and Cao, G.: Processing Range-Monitoring Queries on Heterogeneous Mobile Objects. In IEEE Int’l Conference on Mobile Data Management (MDM’04), pp. 27–38, January (2004).Google Scholar
  6. Gedik, B. and Liu, L., MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System. In EDBT (2004).Google Scholar
  7. Guttman, A. R-tree: A Dynamic Index Structure for spatial Searching. In SIGMOD ‘84, Proceedings of the ACM SIGMOD Conference. ACM Press (1984).Google Scholar
  8. G. R. Hjaltason and H. Samet. Distance Browsing in Spatial Databases. TODS, vol. 24, No.2, pp. 265–318, June (1999).CrossRefGoogle Scholar
  9. Kim, K., Cha, S. K. and Kwon. K.: Optimizing Multidimensional Index Trees for Main Memory Access. In Proc. of SIGMOD (2000).Google Scholar
  10. Kolahdouzan, M.R. and Shahabi, S.: Continuous K Nearest Neighbor Queries in Spatial Network Databases C. Proceedings of the Second Workshop on Spatio-Temporal Database Management (STDBM’04),Toronto, Canada, August (2004).Google Scholar
  11. Lin, D., Jensen, C. S., Ooi, B. C. and Saltenis, S.: Efficient Indexing of the Historical, Present, and Future Positions of Moving Objects," in Proceedings of MDM (2005).Google Scholar
  12. Luyten, K. Coninx, K.: ImogI: Take Control over a Context-aware Electronic Mobile Guide for Museums (2004).Google Scholar
  13. Lynch, K.: The Image of the City. MIT Press, Cambridge (1960).Google Scholar
  14. Merriam-Webster: Merriam-Webster’s Collegiate Dictionary. Merriam-Webster, Inc (2001).Google Scholar
  15. Nievergelt, J., Hinterberger, H., and Sevcik, K., The grid file: An adaptable, Symmetric Multikey File Structure. ACM Transactions on Database Systems, vol.9, pp.38–71 (1984).CrossRefGoogle Scholar
  16. Pfoser, D., Jensen, C.S and Theodoridis, Y.: Novel Approaches to the Indexing of Moving Object Trajectories. Proceedings of the 26th International Conference on Very Large Databases, Cairo, Egypt (2000).Google Scholar
  17. Prabhakar, S., Xia, Y. Kalashnikov, D. V., Aref, W. G. and Hambrusch. S. E.: Query Indexing and Velocity Constrained Indexing: Scalable Techniques for Continuous Queries on Moving Objects. IEEE Trans. Computers, Vol.51, No.10, pp. 1124–1140 (2002).Google Scholar
  18. Raubal, M.: Human Wayfinding in Unfamiliar Buildings: A Simulation with a Cognizing Agent, at the Department of Geography, University of Zurich, Switzerland (2001).Google Scholar
  19. Raubal, M. and Winter, S.: Enriching wayfinding instructions with local landmarks. Egenhofer, M. and Mark, D. (Eds.), Geographic Information Science, Lecture Notes in Computer Science 2002, LNCS vol. 2478, pp. 243—259. Springer (2002).Google Scholar
  20. Saltenis, S., Jensen, C. S. and Leutenegger, S. T. and Lopez, M. A.: Indexing the Positions of Continuously Moving Objects. In SIGMOD Conference (2000).Google Scholar
  21. Sistla, P., Wolfson, O., Chamberlain, S, and Dao, S.: Modeling and Querying Moving objects, in: Proceedings 13th ICDE Conference, pp. 422–432 (1997).Google Scholar
  22. Shekhar S. and Chawla, S.: Spatial Databases: A Tour, Prentice Hall (2003).Google Scholar
  23. Stojanovic, D., Papadopoulos, A.N, Predic, B., Djordjevic-Kajan, S. and Nanopoulos, A.: Continuous Range Monitoring of Mobile Objects in Road Networks, J. Data & Knowledge Engineering, 64, pp. 77–100 (2008).CrossRefGoogle Scholar
  24. Sun, J., Papadias, D., Tao, Y. and Liu, B.: Querying about the Past, the Present, and the Future in Spatio-Temporal Databases. ICDE, pp. 202–213 (2004).Google Scholar
  25. Thakkar, S. and Samet. H, Spatial Data Structures, http://infolab.usc.edu/csci599/Fall2001/note/SpatialDataStructures.ppt (2001).Google Scholar
  26. Xia, Y. and Prabhakar, S.: Q+Rtree: Efficient Indexing for Moving Object Database. Database Systems for Advanced Application. Proceedings. Eighth International Conference on Volume, Issue, pp. 175 –182 (2003).Google Scholar
  27. Zhang, W., Li, J. and Pan H.: Processing Continuous k -Nearest Neighbor Queries in Location- Dependent Application, J. Computer Science and Network Security, 6 No.3 (2006).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Najmeh Samany
    • 1
  • Mohmoud Reza Delavar
    • 2
  • Sara Saeedi
    • 2
  • Reza Aghataher
    • 3
  1. 1.GIS Division Dept. of Surveying and Geomatics Eng.College of Eng.University of Tehran National Cartographic CenterIran
  2. 2.Center of Excellence in Geomatics Eng. and Disaster Management Dept. of Surveying and Geomatics EngCollege of Eng.University of TehranIran
  3. 3.Head of GIS department in National Geographic OrganizationIran

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