Correlative Analytics Methods in Large Scale Network Infrastructure

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Publish Date: 
Thursday, September 27, 2018
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Abstract: 

Mining interesting patterns through the knowledge discovery process is an area of growing interest. Incorporating knowledge and reasoning into existing network infrastructure could result in more efficient, smart and adaptive environments. Huge volumes of data collected on a large data center network presents interesting challenges for analyzing and identifying anomalies such as network misconfiguration and impending service outages. Artificial Intelligence techniques based on association rules, correlative analytics and supervised learning could be efficiently used to target these problems at scale. AI techniques will be presented for generating meaningful insights and identifying trends and patterns impacting the functionality and performance on a larger scale network.

Learning Objectives:
1. Big data analytics at scale
2. Artificial Intelligence methods
3. Data mining for knowledge discovery

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