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Mining system logs to learn error predictors: a case study of a telemetry system

Overview of attention for article published in Empirical Software Engineering, March 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

patent
2 patents
facebook
1 Facebook page

Readers on

mendeley
85 Mendeley
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Article details
Title
Mining system logs to learn error predictors: a case study of a telemetry system
Published in
Empirical Software Engineering, March 2014
DOI 10.1007/s10664-014-9303-2
Authors

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Timeline Attention over time Attention Score history
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Activity
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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Italy 1 1%
Germany 1 1%
Chile 1 1%
Canada 1 1%
Unknown 81 95%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 23 27%
Student > Ph. D. Student 16 19%
Student > Doctoral Student 6 7%
Student > Bachelor 5 6%
Researcher 5 6%
Other 9 11%
Unknown 21 25%
Readers by discipline
Readers by discipline Count As %
Computer Science 45 53%
Engineering 8 9%
Mathematics 2 2%
Business, Management and Accounting 2 2%
Unspecified 1 1%
Other 3 4%
Unknown 24 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 01 June 2021.
All research outputs
#11,631,900
of 33,957,889 outputs
Outputs from Empirical Software Engineering
#348
of 957 outputs
Outputs of similar age
#100,104
of 280,576 outputs
Outputs of similar age from Empirical Software Engineering
#3
of 10 outputs
Altmetric has tracked 33,957,889 research outputs across all sources so far. This one has received more attention than most of these and is in the 64th percentile.
So far Altmetric has tracked 957 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 61% of its peers.
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 280,576 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.