UPI: a primary index for uncertain databases
Proceedings of the VLDB Endowment
Effectively indexing the multi-dimensional uncertain objects for range searching
Proceedings of the 15th International Conference on Extending Database Technology
Indexing uncertain spatio-temporal data
Proceedings of the 21st ACM international conference on Information and knowledge management
HUGVid: handling, indexing and querying of uncertain geo-tagged videos
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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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.