Review Paper on Anomaly Detection in Data Streams. Sandhya Madhuri, G., Yamuna, & Usha Rani, M. In Jyothi, S., Mamatha, D. M., Zhang, Y., & Raju, K. S., editors, Proceedings of the 2nd International Conference on Computational and Bio Engineering, of Lecture Notes in Networks and Systems, pages 721–728, Singapore, 2021. Springer. doi abstract bibtex Anomaly is in general defined as deviation or diversion from the normal. The word anomaly came from the Greek word anomalia which means “uneven” or “irregular”. In our day-to-day lives, we have seen many such irregularities or deviations from normalcy. For example, a condition monitoring system beeps an alarm when it detects any value or parameter of the machine away from the minimum value limit to the maximum value limit, or a credit card fraud alerts the bank and the customer immediately. Now, the crucial task here is how we detect anomalies in data streams. When there is streaming data that is continuously generated from any source, it is called a data stream. The task of finding anomalies from such a stream of data will be a challenging job. In this paper, we will discuss elaborately about data streams and anomaly detection in data streams by reviewing several papers and articles written on this topic.
@inproceedings{sandhya_madhuri_review_2021,
address = {Singapore},
series = {Lecture {Notes} in {Networks} and {Systems}},
title = {Review {Paper} on {Anomaly} {Detection} in {Data} {Streams}},
isbn = {9789811619410},
doi = {10.1007/978-981-16-1941-0_72},
abstract = {Anomaly is in general defined as deviation or diversion from the normal. The word anomaly came from the Greek word anomalia which means “uneven” or “irregular”. In our day-to-day lives, we have seen many such irregularities or deviations from normalcy. For example, a condition monitoring system beeps an alarm when it detects any value or parameter of the machine away from the minimum value limit to the maximum value limit, or a credit card fraud alerts the bank and the customer immediately. Now, the crucial task here is how we detect anomalies in data streams. When there is streaming data that is continuously generated from any source, it is called a data stream. The task of finding anomalies from such a stream of data will be a challenging job. In this paper, we will discuss elaborately about data streams and anomaly detection in data streams by reviewing several papers and articles written on this topic.},
language = {en},
booktitle = {Proceedings of the 2nd {International} {Conference} on {Computational} and {Bio} {Engineering}},
publisher = {Springer},
author = {Sandhya Madhuri, G. and {Yamuna} and Usha Rani, M.},
editor = {Jyothi, S. and Mamatha, D. M. and Zhang, Yu-Dong and Raju, K. Srujan},
year = {2021},
keywords = {Anomaly, Anomaly detection algorithms, Data streams, Outliers},
pages = {721--728},
}
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