<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arian Bär</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Lukasz Golab</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Big Data, IEEE BigData</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Big Data Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Stream Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">network data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">System Performance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Washington D.C., USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The complexity of the Internet has rapidly increased, making it more important and challenging to design scalable network monitoring tools. Network monitoring typically requires rolling data analysis, i.e., continuously and incrementally updating (rolling-over) various reports and statistics over high-volume data streams. In this paper, we describe DBStream, which is an SQL-based system that explicitly supports incremental queries for rolling data analysis. We also present a performance comparison of DBStream with a parallel data processing engine (Spark), showing that, in some scenarios, a single DBStream node can outperform a cluster of ten Spark nodes on rolling network monitoring workloads. Although our performance evaluation is based on network monitoring data, our results can be generalized to other big data problems with high volume and velocity.&lt;/p&gt;</style></abstract></record></records></xml>