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Software Engineering for Self-Adaptive Systems

Overview of attention for book
Attention for Chapter 3: Engineering Self-Adaptive Systems through Feedback Loops
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
1 news outlet
wikipedia
1 Wikipedia page

Citations

dimensions_citation
220 Dimensions

Readers on

mendeley
432 Mendeley
citeulike
9 CiteULike
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Chapter title
Engineering Self-Adaptive Systems through Feedback Loops
Chapter number 3
Book title
Software Engineering for Self-Adaptive Systems
Published in
Lecture notes in computer science, January 2009
DOI 10.1007/978-3-642-02161-9_3
Book ISBNs
978-3-64-202160-2, 978-3-64-202161-9
Authors

Yuriy Brun, Giovanna Di Marzo Serugendo, Cristina Gacek, Holger Giese, Holger Kienle, Marin Litoiu, Hausi Müller, Mauro Pezzè, Mary Shaw

Editors

Betty H. C. Cheng, Rogério de Lemos, Holger Giese, Paola Inverardi, Jeff Magee

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 432 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 7 2%
Germany 6 1%
United Kingdom 6 1%
United States 4 <1%
Brazil 4 <1%
Canada 3 <1%
Netherlands 3 <1%
Austria 2 <1%
Ireland 2 <1%
Other 13 3%
Unknown 382 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 144 33%
Student > Master 77 18%
Researcher 57 13%
Student > Bachelor 31 7%
Student > Doctoral Student 25 6%
Other 63 15%
Unknown 35 8%
Readers by discipline Count As %
Computer Science 296 69%
Engineering 43 10%
Business, Management and Accounting 14 3%
Social Sciences 6 1%
Agricultural and Biological Sciences 5 1%
Other 21 5%
Unknown 47 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 09 September 2016.
All research outputs
#2,291,656
of 22,880,230 outputs
Outputs from Lecture notes in computer science
#459
of 8,130 outputs
Outputs of similar age
#10,703
of 169,456 outputs
Outputs of similar age from Lecture notes in computer science
#8
of 177 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,130 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 94% 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 169,456 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 177 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.