Anomaly Detection Guidelines for Data Streams in Big Data
| Autor: | Victor Muntes, Marc Solé, Giovani Estrada, Annie Ibrahim Rana |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Data stream mining
business.industry Computer science Big data 02 engineering and technology computer.software_genre Data science Field (computer science) Variety (cybernetics) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Data mining Cluster analysis business computer |
| Zdroj: | 2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI). |
| DOI: | 10.1109/iscmi.2016.24 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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