in Cloud Computing]]> seamless scaling, many companies struggle following up with the amount of data generated in terms
of efficient processing and anomaly detection, which is a necessary part of the management of
modern applications. As the record of user behavior, weblogs surely become the research item
related to anomaly detection. Many anomaly detection methods based on automated log analysis
have been proposed. However, not in the context of big data applications where anomalous behavior
needs to be detected in understanding phases prior to modeling a system for such use. Big Data
Analytics often ignores anomalous point due to high volume of data. To address this problem, we
propose a complemented methodology for Big Data Analytics – the Exploratory Data Analysis,
which assists in gaining insight into data relationships without the classical hypothesis modeling. In
that way, we can gain better understanding of the patterns and spot anomalies. Results show that
Exploratory Data Analysis facilitates anomaly detection and the CRISP-DM Business
Understanding phase, making it one of the key steps in the Data Understanding phase.
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