Abstract
A large number of high-dimensional index structures suffer from the so called ’dimensional curse’ problem, i.e., the retrieval performance becomes increasingly degraded as the dimensionality is increased. To solve this problem, the cell-based filtering scheme has been proposed, but it shows a linear decrease in performance as the dimensionality is increased. In this paper, we propose a parallel high-dimensional index structure using the cell-based filtering for multimedia data so as to cope with the linear decrease in retrieval performance. In addition, we devise data insertion, range query and k-NN query processing algorithms which are suitable for the cluster-based parallel architecture. Finally, we show that our parallel index structure achieves good retrieval performance in proportion to the number of servers in the cluster-based architecture and it outperforms a parallel version of the VA-File when the dimensionality is over 10.
This work is financially supported by the Ministry of Education and Human Resources Development(MOE), the Ministry of Commerce, Industry and Energy(MOCIE) and the Ministry of Labor(MOLAB) though the fostering project of the Lab of Excellency.
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Chang, JW., Kim, YK., Kim, YJ. (2006). Parallel High-Dimensional Index Structure Using Cell-Based Filtering for Multimedia Data. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds) Frontiers of High Performance Computing and Networking – ISPA 2006 Workshops. ISPA 2006. Lecture Notes in Computer Science, vol 4331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11942634_80
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DOI: https://doi.org/10.1007/11942634_80
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