<?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%">Francisco Maia</style></author><author><style face="normal" font="default" size="100%">Miguel Matos</style></author><author><style face="normal" font="default" size="100%">Fábio Coelho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Quantifiable Eventual Consistency</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 6th International Conference on Cloud Computing and Services Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/fmaia/files/datadiversityconvergence_2016_5.pdf</style></url></related-urls></urls><pages><style face="normal" font="default" size="100%">368-370</style></pages><isbn><style face="normal" font="default" size="100%">978-989-758-182-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In the pursuit of highly available systems, storage systems began offering eventually consistent data models. These models are suitable for a number of applications but not applicable for all. In this paper we discuss a system that can offer a eventually consistent data model but can also, when needed, offer a strong consistent one.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
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