ADCMO: An Anomaly Detection Approach Based on Local Outlier Factor for Continuously Monitored Object

Su, Shubin; Xiao, Limin; Ruan, Li; Xu, Rongbin; Li, Shupan; Wang, Zhaokai; He, Qigong; Li, Wei

doi:10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00127

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Su et al [76] proposed abnormal conditions detection using the continuously monitored objects approach for global outlier detection. The tropical atmosphere ocean dataset and the generated dataset were utilized for the experiment, and the results were compared with those achieved using the incremental local outlier factor algorithm in terms of time requirements, space requirements, and F-score.…”

Section: Unclassified Techniquesmentioning

confidence: 99%

Cleaning Big Data Streams: A Systematic Literature Review

2023

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In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overcome these challenges, such as cleaning big data in real time. This systematic literature review presents recently developed techniques that have been used for the cleaning process and for each data cleaning issue. Following the PRISMA framework, four databases are searched, namely IEEE Xplore, ACM Library, Scopus, and Science Direct, to select relevant studies. After selecting the relevant studies, we identify the techniques that have been utilized to clean big data streams and the evaluation methods that have been used to examine their efficiency. Also, we define the cleaning issues that may appear during the cleaning process, namely missing values, duplicated data, outliers, and irrelevant data. Based on our study, the future directions of cleaning big data streams are identified.

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Section: Unclassified Techniquesmentioning

confidence: 99%