Issue No. 09 - September (2010 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.77
Ying Zhang , University of New South Wales, Sydney
Xuemin Lin , University of New South Wales, Sydney
Jianmin Wang , Tsinghua University, China
Wenjie Zhang , University of New South Wales, Sydney
Qianlu Lin , University of New South Wales, Sydney
With the rapid development of various optical, infrared, and radar sensors and GPS techniques, there are a huge amount of multidimensional uncertain data collected and accumulated everyday. Recently, considerable research efforts have been made in the field of indexing, analyzing, and mining uncertain data. As shown in a recent book [CHECK END OF SENTENCE] on uncertain data, in order to efficiently manage and mine uncertain data, effective indexing techniques are highly desirable. Based on the observation that the existing index structures for multidimensional data are sensitive to the size or shape of uncertain regions of uncertain objects and the queries, in this paper, we introduce a novel R-Tree-based inverted index structure, named UI-Tree, to efficiently support various queries including range queries, similarity joins, and their size estimation, as well as top-k range query, over multidimensional uncertain objects against continuous or discrete cases. Comprehensive experiments are conducted on both real data and synthetic data to demonstrate the efficiency of our techniques.
Uncertain, index, range query, partition.
Ying Zhang, Xuemin Lin, Jianmin Wang, Wenjie Zhang, Qianlu Lin, "Effectively Indexing the Uncertain Space", IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 1247-1261, September 2010, doi:10.1109/TKDE.2010.77