| Title |
A survey of open source tools for machine learning with big data in the Hadoop ecosystem
|
|---|---|
| Published in |
Journal of Big Data, November 2015
|
| DOI | 10.1186/s40537-015-0032-1 |
| Authors |
Sara Landset, Taghi M. Khoshgoftaar, Aaron N. Richter, Tawfiq Hasanin |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 26% |
| France | 3 | 16% |
| India | 2 | 11% |
| Chile | 1 | 5% |
| Switzerland | 1 | 5% |
| Estonia | 1 | 5% |
| Unknown | 6 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 17 | 89% |
| Scientists | 2 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 758 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 2 | <1% |
| United States | 2 | <1% |
| Brazil | 2 | <1% |
| Germany | 1 | <1% |
| Switzerland | 1 | <1% |
| Italy | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Portugal | 1 | <1% |
| Belgium | 1 | <1% |
| Other | 3 | <1% |
| Unknown | 743 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 176 | 23% |
| Student > Ph. D. Student | 130 | 17% |
| Researcher | 56 | 7% |
| Student > Bachelor | 54 | 7% |
| Student > Postgraduate | 36 | 5% |
| Other | 147 | 19% |
| Unknown | 159 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 378 | 50% |
| Engineering | 86 | 11% |
| Business, Management and Accounting | 30 | 4% |
| Social Sciences | 21 | 3% |
| Agricultural and Biological Sciences | 10 | 1% |
| Other | 59 | 8% |
| Unknown | 174 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 20 April 2017.
All research outputs
#2,155,880
of 23,577,761 outputs
Outputs from Journal of Big Data
#46
of 356 outputs
Outputs of similar age
#32,332
of 287,104 outputs
Outputs of similar age from Journal of Big Data
#3
of 7 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 356 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done well, scoring higher than 87% 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 287,104 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.