Abstract
Mobile object index should support efficient update operations besides efficient query operations. In this paper, we consider the issue of the efficient updating of mobile object index. Based on a model for the mobile data, we introduce a method of incorporating statistical information of the regions covered by the mobile objects into feature vectors. We then propose a novel architecture of mobile object index, where R-tree is used to index the occupied regions instead of the mobile objects themselves and extreme learning machine (ELM) is used to classify the regions. Further, we describe several related algorithms and the update strategy based on the classification of the regions. The proposed strategy and algorithms are evaluated in a simulated environment. The experiments demonstrate that the proposed update strategy based on region classification using ELM can achieve higher performance with respect to I/O operations. Compared to the strategy without region classification, the proposed method can reduce the number of I/O operations more than 80%.
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This paper was supported by National Natural Science Foundation of China (Grant No. 61173030, 60803026, 61073063).
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Wang, B., Wang, G., Li, J. et al. Update strategy based on region classification using ELM for mobile object index. Soft Comput 16, 1607–1615 (2012). https://doi.org/10.1007/s00500-012-0821-9
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DOI: https://doi.org/10.1007/s00500-012-0821-9