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» Efficient Discovery of Confounders in Large Data Sets
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SENSYS
2010
ACM
13 years 6 months ago
sMAP: a simple measurement and actuation profile for physical information
As more and more physical information becomes available, a critical problem is enabling the simple and efficient exchange of this data. We present our design for a simple RESTful ...
Stephen Dawson-Haggerty, Xiaofan Jiang, Gilman Tol...
GIS
2005
ACM
14 years 10 months ago
ASPEN: an adaptive spatial peer-to-peer network
Geographic Information Systems (GIS) are increasingly managing very large sets of data and hence a centralized data repository may not always provide the most scalable solution. H...
Haojun Wang, Roger Zimmermann, Wei-Shinn Ku
KDD
2009
ACM
163views Data Mining» more  KDD 2009»
14 years 9 months ago
Large-scale graph mining using backbone refinement classes
We present a new approach to large-scale graph mining based on so-called backbone refinement classes. The method efficiently mines tree-shaped subgraph descriptors under minimum f...
Andreas Maunz, Christoph Helma, Stefan Kramer
ICML
2009
IEEE
14 years 9 months ago
A convex formulation for learning shared structures from multiple tasks
Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. In this paper, we consider the problem of learning shared s...
Jianhui Chen, Lei Tang, Jun Liu, Jieping Ye
VLDB
2007
ACM
181views Database» more  VLDB 2007»
14 years 9 months ago
STAR: Self-Tuning Aggregation for Scalable Monitoring
We present STAR, a self-tuning algorithm that adaptively sets numeric precision constraints to accurately and efficiently answer continuous aggregate queries over distributed data...
Navendu Jain, Michael Dahlin, Yin Zhang, Dmitry Ki...