Abstract
Supporting frequent updates is a key challenge in moving object indexing. Most of the existing work regards the update as an individual process for each object, and a large number of separate updates are issued respectively in update-intensive environments. In this paper, we propose the bulkloading updates for moving objects (BLU). Based on a common framework, we propose three bulkloading schemes of different spatial biases. By grouping the objects with near positions, BLU prefetches the nodes accessed on the shared update path and combines multiple disk accesses to the same node into one, which avoids I/O overhead for objects within the same group. We also propose a novel MBR-driven flushing algorithm, which utilizes the dynamic spatial correlation and improves the buffer hit ratio. The theoretical analysis and experimental evaluation demonstrate that BLU achieves the good update performance and does not affect the query performance.
This work was supported in part by Natural Science Foundation of China under grant number No. 60503035.
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Wang, X., Sun, W., Wang, W. (2006). Bulkloading Updates for Moving Objects. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_27
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DOI: https://doi.org/10.1007/11775300_27
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