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» A compact data structure for representing a dynamic multiset
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KDD
2007
ACM
159views Data Mining» more  KDD 2007»
14 years 9 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at org...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson
DEBU
2010
127views more  DEBU 2010»
13 years 6 months ago
Provenance in ORCHESTRA
Sharing structured data today requires agreeing on a standard schema, then mapping and cleaning all of the data to achieve a single queriable mediated instance. However, for setti...
Todd J. Green, Grigoris Karvounarakis, Zachary G. ...
BMCBI
2010
223views more  BMCBI 2010»
13 years 3 months ago
MetNetGE: interactive views of biological networks and ontologies
Background: Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large...
Ming Jia, Suh-Yeon Choi, Dirk Reiners, Eve Syrkin ...
UAI
2003
13 years 10 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
INFOCOM
2006
IEEE
14 years 2 months ago
Theory and Network Applications of Dynamic Bloom Filters
Abstract— A bloom filter is a simple, space-efficient, randomized data structure for concisely representing a static data set, in order to support approximate membership querie...
Deke Guo, Jie Wu, Honghui Chen, Xueshan Luo