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Anomaly detection based on system text logs of virtual network functions

Overview of attention for article published in Big Data Research, November 2024
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Title
Anomaly detection based on system text logs of virtual network functions
Published in
Big Data Research, November 2024
DOI 10.1016/j.bdr.2024.100485
Authors

Daniela N. Rim, DongNyeong Heo, Chungjun Lee, Sukhyun Nam, Jae-Hyoung Yoo, James Won-Ki Hong, Heeyoul Choi

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 August 2024.
All research outputs
#20,815,877
of 26,447,081 outputs
Outputs from Big Data Research
#76
of 130 outputs
Outputs of similar age
#4,026
of 5,363 outputs
Outputs of similar age from Big Data Research
#1
of 1 outputs
Altmetric has tracked 26,447,081 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 130 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 5,363 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them