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» Representing Data Quality for Streaming and Static Data
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KDD
2005
ACM
147views Data Mining» more  KDD 2005»
15 years 10 months ago
Combining proactive and reactive predictions for data streams
Mining data streams is important in both science and commerce. Two major challenges are (1) the data may grow without limit so that it is difficult to retain a long history; and (...
Ying Yang, Xindong Wu, Xingquan Zhu
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
16 years 4 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
DATAMINE
2006
230views more  DATAMINE 2006»
15 years 4 months ago
Mining top-K frequent itemsets from data streams
Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to ...
Raymond Chi-Wing Wong, Ada Wai-Chee Fu
ACL
2012
13 years 6 months ago
Named Entity Disambiguation in Streaming Data
The named entity disambiguation task is to resolve the many-to-many correspondence between ambiguous names and the unique realworld entity. This task can be modeled as a classifi...
Alexandre Davis, Adriano Veloso, Altigran Soares d...
ICSE
2004
IEEE-ACM
16 years 4 months ago
A Hybrid Architectural Style for Distributed Parallel Processing of Generic Data Streams
Immersive, interactive applications grouped under the concept of Immersipresence require on-line processing and mixing of multimedia data streams and structures. One critical issu...
Alexandre R. J. François