PROUD: a probabilistic approach to processing similarity queries over uncertain data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A review on time series data mining
Engineering Applications of Artificial Intelligence
Top-k similarity search on uncertain trajectories
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
A probabilistic approach to correlation queries in uncertain time series data
Proceedings of the 21st ACM international conference on Information and knowledge management
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In many privacy preserving applications such as Location-Based Services (LBS), medical data analysis, and data sequence matching, users often deliberately disturb the original data in order to avoid the release of their private information. Although these disturbed cloaked data cannot reveal the privacy information of individual users, they can still help perform some data mining tasks such as data classification. In this paper, we study one important and fundamental query predicate, that is, to find the cloaked time series that are similar to a query pattern. In this paper, we formalize such similarity search problem over the cloaked time series, and propose a novel approach to index the cloaked series, which can facilitate the similarity query.