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JMLR
2010
134views more  JMLR 2010»
14 years 4 months ago
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
Jan Lemeire, Kris Steenhaut
PERCOM
2007
ACM
15 years 9 months ago
Structural Learning of Activities from Sparse Datasets
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
Fahd Albinali, Nigel Davies, Adrian Friday
FGCS
2011
153views more  FGCS 2011»
14 years 4 months ago
Representing distributed systems using the Open Provenance Model
From the World Wide Web to supply chains and scientific simulations, distributed systems are a widely used and important approach to building computational systems. Tracking prov...
Paul T. Groth, Luc Moreau
SEMWEB
2010
Springer
14 years 7 months ago
Summary Models for Routing Keywords to Linked Data Sources
The proliferation of linked data on the Web paves the way to a new generation of applications that exploit heterogeneous data from different sources. However, because this Web of d...
Thanh Tran, Lei Zhang, Rudi Studer
WMCSA
2003
IEEE
15 years 3 months ago
Proximity Mining: Finding Proximity using Sensor Data History
Emerging ubiquitous and pervasive computing applications often need to know where things are physically located. To meet this need, many locationsensing systems have been develope...
Toshihiro Takada, Satoshi Kurihara, Toshio Hirotsu...