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» Practical Preference Relations for Large Data Sets
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KDID
2004
481views Database» more  KDID 2004»
13 years 9 months ago
Models and Indices for Integrating Unstructured Data with a Relational Database
Abstract. Database systems are islands of structure in a sea of unstructured data sources. Several real-world applications now need to create bridges for smooth integration of semi...
Sunita Sarawagi
ICCV
2001
IEEE
14 years 10 months ago
Feature Selection from Huge Feature Sets
The number of features that can be computed over an image is, for practical purposes, limitless. Unfortunately, the number of features that can be computed and exploited by most c...
José Bins, Bruce A. Draper
ICDE
2006
IEEE
201views Database» more  ICDE 2006»
14 years 9 months ago
Counting at Large: Efficient Cardinality Estimation in Internet-Scale Data Networks
Counting in general, and estimating the cardinality of (multi-) sets in particular, is highly desirable for a large variety of applications, representing a foundational block for ...
Nikos Ntarmos, Peter Triantafillou, Gerhard Weikum
FGR
2004
IEEE
133views Biometrics» more  FGR 2004»
14 years 5 days ago
Finding Temporal Patterns by Data Decomposition
We present a new unsupervised learning technique for the discovery of temporal clusters in large data sets. Our method performs hierarchical decomposition of the data to find stru...
David C. Minnen, Christopher Richard Wren
CIKM
2010
Springer
13 years 7 months ago
FacetCube: a framework of incorporating prior knowledge into non-negative tensor factorization
Non-negative tensor factorization (NTF) is a relatively new technique that has been successfully used to extract significant characteristics from polyadic data, such as data in s...
Yun Chi, Shenghuo Zhu