Declustering Spatial Objects by Clustering for Parallel Disks

  • Hak-Cheol Kim
  • Ki-Joune Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2113)


In this paper, we propose an efficient declustering algorithm which is adaptable in different data distribution. Previous declustering algorithms have a potential drawback by assuming data distribution is uniform. However, our method shows a good declustering performance for spatial data regardless of data distribution by taking it into consideration. First, we apply a spatial clustering algorithm to find the distribution in the underlying data and then allocate a disk page to each unit of cluster. Second, we analyize the effect of outliers on the performance of declustering algorithm and propose to handle them separately. Experimental results show that these approaches outperform traditional declustering algorithms based on tiling and mapping function such as DM, FX, HCAM and Golden Ratio Sequence.


Spatial Object Average Response Time Skewed Data Uniform Data Parallel Disk 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Hak-Cheol Kim
    • 1
  • Ki-Joune Li
    • 1
  1. 1.Dept of Computer SciencePusan National UniversityKorea

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