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» Efficient Discovery of Confounders in Large Data Sets
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VLDB
1999
ACM
159views Database» more  VLDB 1999»
14 years 2 months ago
Aggregation Algorithms for Very Large Compressed Data Warehouses
Many efficient algorithms to compute multidimensional aggregation and Cube for relational OLAP have been developed. However, to our knowledge, there is nothing to date in the lite...
Jianzhong Li, Doron Rotem, Jaideep Srivastava
PADL
2009
Springer
14 years 11 months ago
Using Bloom Filters for Large Scale Gene Sequence Analysis in Haskell
Analysis of biological data often involves large data sets and computationally expensive algorithms. Databases of biological data continue to grow, leading to an increasing demand ...
Ketil Malde, Bryan O'Sullivan
INFOVIS
2005
IEEE
14 years 4 months ago
Importance-Driven Visualization Layouts for Large Time Series Data
Time series are an important type of data with applications in virtually every aspect of the real world. Often a large number of time series have to be monitored and analyzed in p...
Ming C. Hao, Umeshwar Dayal, Daniel A. Keim, Tobia...
WWW
2009
ACM
14 years 5 months ago
Social search and discovery using a unified approach
: This talk describes a research project exploring new ways for augmenting search using multiple types and sources of social information. Our goal is to allow searching for all obj...
Einat Amitay, David Carmel, Nadav Har'El, Shila Of...
AAAI
2008
14 years 21 days ago
Markov Blanket Feature Selection for Support Vector Machines
Based on Information Theory, optimal feature selection should be carried out by searching Markov blankets. In this paper, we formally analyze the current Markov blanket discovery ...
Jianqiang Shen, Lida Li, Weng-Keen Wong