<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carlos Baquero Moreno</style></author><author><style face="normal" font="default" size="100%">Paulo Sérgio Almeida</style></author><author><style face="normal" font="default" size="100%">Miguel Borges</style></author><author><style face="normal" font="default" size="100%">Paulo Jesus</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spectra: Robust estimation of distribution functions in networks</style></title><secondary-title><style face="normal" font="default" size="100%">Distributed Applications and Interoperable Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-30823-9_8</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/cbm/files/1204.1373.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Stockholm, Sweden</style></pub-location><volume><style face="normal" font="default" size="100%">7272</style></volume><pages><style face="normal" font="default" size="100%">96–103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The distributed aggregation of simple aggregates such as minima/maxima, counts, sums and averages have been studied in the past and are important tools for distributed algorithms and network co- ordination. Nonetheless, this kind of aggregates may not be comprehen- sive enough to characterize biased data distributions or when in presence of outliers, making the case for richer estimates.&lt;br /&gt;
This work presents Spectra, a distributed algorithm for the estimation of distribution functions over large scale networks. The estimate is available at all nodes and the technique depicts important properties: robustness when exposed to high levels of message loss, fast convergence speed and fine precision in the estimate. It can also dynamically cope with changes of the sampled local property and with churn, without requiring restarts. The proposed approach is experimentally evaluated and contrasted to a competing state of the art distribution aggregation technique.&lt;/p&gt;
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