Volume XLII-2/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 69-73, 2016
https://doi.org/10.5194/isprs-archives-XLII-2-W1-69-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 69-73, 2016
https://doi.org/10.5194/isprs-archives-XLII-2-W1-69-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Oct 2016

26 Oct 2016

IMPROVING NEAREST NEIGHBOUR SEARCH IN 3D SPATIAL ACCESS METHOD

A. Suhaibaha1, A. A. Rahman1, U. Uznir1, F. Anton2, and D. Mioc2 A. Suhaibaha et al.
  • 1Geospatial Information Infrastructure (GeoI2) Research Lab., Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
  • 2Dept. of Geodesy, National Space Institute, Technical University of Denmark, Elektrovej 328, 2800 Kgs. Lyngby, Denmark

Keywords: Nearest Neighbour, 3D Data Clustering, 3D Spatial Access Method, 3D GIS, Data Management, Information Retrieval

Abstract. Nearest Neighbour (NN) is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO) operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN) search. The tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized.