Name
Abstract | ||
|---|---|---|
The need for fast on-line algorithms to analyze high data-rate measurements is a vital element in production settings. Given the ever-increasing number of data sources coupled with increasing complexity of applications, and workload patterns, anomaly detection methods should be light-weight and must operate in real-time. In many modern applications, data arrive in a streaming fashion. Therefore, the underlying assumption of classical methods that the data is a sample from a stable distribution is not valid, and Gaussian and non-parametric based methods such as the control chart and boxplot are inadequate. Streaming data is an ever-changing superposition of distributions. Detection of such changes in real-time is one of the fundamental challenges. We propose low-complexity robust modifications to the conventional Tukey boxplot based on fast highly efficient robust estimates of scale. Results using synthetic as well as real-world data show that our methods outperform the Tukey boxplot and methods based on Gaussian limits. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638919 | 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
robustness, boxplot, outlier | Data mining,Anomaly detection,Data visualization,Superposition principle,Computer science,Outlier,Gaussian,Control chart,Streaming data,Stable distribution | Conference |
ISSN | Citations | PageRank |
1520-6149 | 3 | 0.59 |
References | Authors | |
1 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georgy Shevlyakov | 1 | 55 | 8.93 |
| Kliton Andrea | 2 | 4 | 0.97 |
| Choudur Lakshminarayan | 3 | 99 | 6.83 |
| Pavel Smirnov | 4 | 3 | 1.27 |
| Alexander Ulanov | 5 | 65 | 9.64 |
| Natalia Vassilieva | 6 | 34 | 5.50 |