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Tapestry: A Resilient Global-scale Overlay for Service Deployment
- IEEE Journal on Selected Areas in Communications
, 2004
"... We present Tapestry, a peer-to-peer overlay routing infrastructure offering efficient, scalable, locationindependent routing of messages directly to nearby copies of an object or service using only localized resources. Tapestry supports a generic Decentralized Object Location and Routing (DOLR) API ..."
Abstract
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Cited by 593 (14 self)
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using a self-repairing, softstate based routing layer. This paper presents the Tapestry architecture, algorithms, and implementation. It explores the behavior of a Tapestry deployment on PlanetLab, a global testbed of approximately 100 machines. Experimental results show that Tapestry exhibits stable
2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late Nineteenth Century
- J. Geophysical Research
"... data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1 ° latitude-longitude grid ..."
Abstract
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Cited by 517 (3 self)
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data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1 ° latitude-longitude grid
OPTICS: Ordering Points To Identify the Clustering Structure
, 1999
"... Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of ..."
Abstract
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Cited by 511 (49 self)
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of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-data sets there does not even exist a global parameter setting for which the result of the clustering algorithm describes
Okapi at TREC-3
, 1996
"... this document length correction factor is #global": it is added at the end, after the weights for the individual terms have been summed, and is independentofwhich terms match. ..."
Abstract
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Cited by 593 (5 self)
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this document length correction factor is #global": it is added at the end, after the weights for the individual terms have been summed, and is independentofwhich terms match.
Route Packets, Not Wires: On-Chip Interconnection Networks
, 2001
"... Using on-chip interconnection networks in place of ad-hoc global wiring structures the top level wires on a chip and facilitates modular design. With this approach, system modules (processors, memories, peripherals, etc...) communicate by sending packets to one another over the network. The structur ..."
Abstract
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Cited by 864 (10 self)
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Using on-chip interconnection networks in place of ad-hoc global wiring structures the top level wires on a chip and facilitates modular design. With this approach, system modules (processors, memories, peripherals, etc...) communicate by sending packets to one another over the network
Mixtures of Probabilistic Principal Component Analysers
, 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
Abstract
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Cited by 537 (6 self)
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Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a
Bayesian Density Estimation and Inference Using Mixtures
- Journal of the American Statistical Association
, 1994
"... We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation, and are exemplified by special cases where data are modelled as a sample from mixtures of normal distributions. Efficien ..."
Abstract
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Cited by 652 (18 self)
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. Efficient simulation methods are used to approximate various prior, posterior and predictive distributions. This allows for direct inference on a variety of practical issues, including problems of local versus global smoothing, uncertainty about density estimates, assessment of modality, and the inference
Locally weighted learning
- ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
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