Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Anomaly/outlier detection is an important area of machine learning which finds its application in intrusion-detection, fraud-detection, etc. In recent times, the focus of data analytics has shifted to big data analytics, i.e., analytics on large-scale data and fast-moving data streams. The traditional data processing tools and algorithms are not able to handle big data, so, there is a need of algorithms to be implemented in a parallel model like MapReduce to solve this problem. In this study, the researchers implement frequency-based algorithm on Spark MapReduce as a scalable and accurate solution for anomaly detection on large-scale as well as streaming datasets.
How to cite this article:
Adeel S. Hashmi and Tanvir Ahmad. Frequency-Based Fast Algorithm for Anomaly Detection in Big Data.
DOI: https://doi.org/10.36478/jeasci.2017.7389.7392
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2017.7389.7392