The Efficient Way of Detecting Anomalies in Large Scale Streaming Data
These days many companies has marketed the big data streams in numerous applications including industry, Internet of Things and telecommunication. The stream of data produced by these applications may contain the values which are not normal. These values are called as anomalies. A lot of work has be...
Main Authors: | Sheeraz Lighari, Dil Muhammad Akbar Hussain |
---|---|
Format: | Article |
Language: | English |
Published: |
University of Sindh
2018-07-01
|
Series: | University of Sindh Journal of Information and Communication Technology |
Subjects: | |
Online Access: | http://sujo.usindh.edu.pk/index.php/USJICT/article/view/4453/pdf |
Similar Items
-
Deterministic Coresets for k-Means of Big
Sparse Data
by: Artem Barger, et al.
Published: (2020-04-01) -
Real-time Outlier Detection using Unbounded Data Streaming and Machine Learning
by: Åkerström, Emelie
Published: (2020) -
Clustering Approach Based on Mini Batch Kmeans for Intrusion Detection System Over Big Data
by: Kai Peng, et al.
Published: (2018-01-01) -
Hardware Architecture Proposal for TEDA Algorithm to Data Streaming Anomaly Detection
by: Lucileide M. D. Da Silva, et al.
Published: (2021-01-01) -
Anomalies Detection Using Isolation in Concept-Drifting Data Streams
by: Maurras Ulbricht Togbe, et al.
Published: (2021-01-01)