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Defining the execution semantics of stream processing engines

Overview of attention for article published in Journal of Big Data, April 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
46 Mendeley
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Title
Defining the execution semantics of stream processing engines
Published in
Journal of Big Data, April 2017
DOI 10.1186/s40537-017-0072-9
Authors

Lorenzo Affetti, Riccardo Tommasini, Alessandro Margara, Gianpaolo Cugola, Emanuele Della Valle

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 45 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 26%
Student > Master 11 24%
Researcher 6 13%
Student > Doctoral Student 4 9%
Student > Postgraduate 2 4%
Other 6 13%
Unknown 5 11%
Readers by discipline Count As %
Computer Science 30 65%
Business, Management and Accounting 4 9%
Engineering 3 7%
Materials Science 1 2%
Decision Sciences 1 2%
Other 0 0%
Unknown 7 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 May 2017.
All research outputs
#8,569,009
of 15,568,554 outputs
Outputs from Journal of Big Data
#91
of 203 outputs
Outputs of similar age
#120,809
of 270,004 outputs
Outputs of similar age from Journal of Big Data
#1
of 1 outputs
Altmetric has tracked 15,568,554 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 203 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has gotten more attention than average, scoring higher than 51% 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 270,004 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 53% of its contemporaries.
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