Anomaly Detection Guidelines for Data Streams in Big Data

Real time data analysis in data streams is a highly challenging area in big data. The surge in big data techniques has recently attracted considerable interest to the detection of significant changes or anomalies in data streams. There is a variety of literature across a number of fields relevant to...

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Bibliographic Details
Published in2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) pp. 94 - 98
Main Authors Ibrahim Rana, Annie, Estrada, Giovani, Sole, Marc, Muntes, Victor
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Anomaly detection
Big Data
Clustering algorithms
Decision trees
time series
Time series analysis
Training
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Summary:Real time data analysis in data streams is a highly challenging area in big data. The surge in big data techniques has recently attracted considerable interest to the detection of significant changes or anomalies in data streams. There is a variety of literature across a number of fields relevant to anomaly detection. The growing number of techniques, from seemingly disconnected areas, prevents a comprehensive review. Many interesting techniques may therefore remain largely unknown to the anomaly detection community at large. The survey presents a compact, but comprehensive overview of diverse strategies for anomaly detection in evolving data streams. A number of recommendations based performance and applicability to use cases are provided. We expect that our classification and recommendations will provide useful guidelines to practitioners in this rapidly evolving field.
DOI:10.1109/ISCMI.2016.24