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 <title>Automated monitoring and debugging of large scale manycore heterogeneous systems - detection of anomaly in network systems</title>
 <link>https://ahls.dorsal.polymtl.ca/taxonomy/term/51</link>
 <description></description>
 <language>en</language>
<item>
 <title>Mining Telecom System Logs to Facilitate Debugging Tasks,</title>
 <link>https://ahls.dorsal.polymtl.ca/node/121</link>
 <description>&lt;div class=&quot;field field-name-body field-type-text-with-summary field-label-hidden&quot;&gt;&lt;div class=&quot;field-items&quot;&gt;&lt;div class=&quot;field-item even&quot; property=&quot;content:encoded&quot;&gt;&lt;p&gt;A. Larsson, A. Hamou-Lhadj, “Mining Telecom System Logs to Facilitate Debugging Tasks,” In Proc. of the 29th International Conference on Software Maintenance (ICSM’13), Eindhoven, The Netherlands, 2013.&lt;/p&gt;
&lt;p&gt;Telecommunication systems are monitored continuously to ensure quality and continuity of service. When an error or an abnormal behaviour occurs, software engineers resort to the analysis of the generated logs for troubleshooting. The problem is that, even for a small system, the log data generated after running the system for a period of time can be considerably large. There is a need to automatically mine important information from this data. There exist studies that aim to do just that, but their focus has been mainly on software applications, paying little attention to network information used by telecom systems. In this paper, we show how data mining techniques, more particularly the ones based on mining frequent itemsets, can be used to extract patterns that characterize the main behaviour of the traced scenarios. We show the effectiveness of our approach through a representative study conducted in an industrial setting.&lt;/p&gt;
&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;field field-name-field-tags field-type-taxonomy-term-reference field-label-above clearfix&quot;&gt;&lt;h3 class=&quot;field-label&quot;&gt;Tags: &lt;/h3&gt;&lt;ul class=&quot;links&quot;&gt;&lt;li class=&quot;taxonomy-term-reference-0&quot; rel=&quot;dc:subject&quot;&gt;&lt;a href=&quot;/taxonomy/term/50&quot; typeof=&quot;skos:Concept&quot; property=&quot;rdfs:label skos:prefLabel&quot; datatype=&quot;&quot;&gt;Trace analysis&lt;/a&gt;&lt;/li&gt;&lt;li class=&quot;taxonomy-term-reference-1&quot; rel=&quot;dc:subject&quot;&gt;&lt;a href=&quot;/taxonomy/term/51&quot; typeof=&quot;skos:Concept&quot; property=&quot;rdfs:label skos:prefLabel&quot; datatype=&quot;&quot;&gt;detection of anomaly in network systems&lt;/a&gt;&lt;/li&gt;&lt;li class=&quot;taxonomy-term-reference-2&quot; rel=&quot;dc:subject&quot;&gt;&lt;a href=&quot;/taxonomy/term/52&quot; typeof=&quot;skos:Concept&quot; property=&quot;rdfs:label skos:prefLabel&quot; datatype=&quot;&quot;&gt;data mining&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;</description>
 <pubDate>Wed, 13 Nov 2013 22:46:52 +0000</pubDate>
 <dc:creator>ahamou-lhadj</dc:creator>
 <guid isPermaLink="false">121 at https://ahls.dorsal.polymtl.ca</guid>
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