Recently the academic communities have paid more attention to the queries and mining on uncertain data. In the tasks such as clustering or nearest-neighbor queries, expected distan...
We present PROUD - A PRObabilistic approach to processing similarity queries over Uncertain Data streams, where the data streams here are mainly time series streams. In contrast t...
Mi-Yen Yeh, Kun-Lung Wu, Philip S. Yu, Ming-Syan C...
In this paper we propose a data model for representing moving objects with uncertain positions in database systems. It is called the Moving Objects Spatio-Temporal (MOST) data mod...
A. Prasad Sistla, Ouri Wolfson, Sam Chamberlain, S...
Abstract— In this paper, we propose a novel, effective and efficient probabilistic pruning criterion for probabilistic similarity queries on uncertain data. Our approach support...
Thomas Bernecker, Tobias Emrich, Hans-Peter Kriege...
Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from mea...
Jiangtao Ren, Sau Dan Lee, Xianlu Chen, Ben Kao, R...