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» Efficient join processing over uncertain data
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Publication
344views
12 years 9 months ago
Top-k Similarity Join over Multi-valued Objects
The top-k similarity joins have been extensively studied and used in a wide spectrum of applications such as information retrieval, decision making, spatial data analysis and dat...
Wenjie Zhang, Jing Xu, Xin Liang, Ying Zhang, Xuem...
EDBT
2009
ACM
173views Database» more  EDBT 2009»
14 years 3 months ago
PROUD: a probabilistic approach to processing similarity queries over uncertain data streams
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...
SSDBM
2008
IEEE
111views Database» more  SSDBM 2008»
14 years 5 months ago
iJoin: Importance-Aware Join Approximation over Data Streams
We consider approximate join processing over data streams when memory limitations cause incoming tuples to overflow the available space, precluding exact processing. Selective evi...
Dhananjay Kulkarni, Chinya V. Ravishankar
TKDE
2002
127views more  TKDE 2002»
13 years 10 months ago
Efficient Join-Index-Based Spatial-Join Processing: A Clustering Approach
A join-index is a data structure used for processing join queries in databases. Join-indices use precomputation techniques to speed up online query processing and are useful for da...
Shashi Shekhar, Chang-Tien Lu, Sanjay Chawla, Siva...
ICDE
2008
IEEE
161views Database» more  ICDE 2008»
15 years 6 days ago
Efficiently Answering Probabilistic Threshold Top-k Queries on Uncertain Data
on Uncertain Data (Extended Abstract) Ming Hua Jian Pei Wenjie Zhang Xuemin Lin Simon Fraser University, Canada The University of New South Wales & NICTA {mhua, jpei}@cs.sfu.c...
Ming Hua, Jian Pei, Wenjie Zhang, Xuemin Lin