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.
CITATION STYLE
Kim, H. C., & Li, K. J. (2001). Declustering spatial objects by clustering for parallel disks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2113, pp. 450–459). Springer Verlag. https://doi.org/10.1007/3-540-44759-8_45
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