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Efficient algorithms for fast integration on large data sets from multiple sources

Overview of attention for article published in BMC Medical Informatics and Decision Making, June 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
3 tweeters
patent
1 patent

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
49 Mendeley
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Title
Efficient algorithms for fast integration on large data sets from multiple sources
Published in
BMC Medical Informatics and Decision Making, June 2012
DOI 10.1186/1472-6947-12-59
Pubmed ID
Authors

Tian Mi, Sanguthevar Rajasekaran, Robert Aseltine

Abstract

Recent large scale deployments of health information technology have created opportunities for the integration of patient medical records with disparate public health, human service, and educational databases to provide comprehensive information related to health and development. Data integration techniques, which identify records belonging to the same individual that reside in multiple data sets, are essential to these efforts. Several algorithms have been proposed in the literatures that are adept in integrating records from two different datasets. Our algorithms are aimed at integrating multiple (in particular more than two) datasets efficiently.

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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
India 1 2%
Canada 1 2%
Unknown 46 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 27%
Researcher 8 16%
Student > Postgraduate 5 10%
Other 4 8%
Student > Master 4 8%
Other 10 20%
Unknown 5 10%
Readers by discipline Count As %
Medicine and Dentistry 12 24%
Computer Science 12 24%
Engineering 6 12%
Biochemistry, Genetics and Molecular Biology 3 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 5 10%
Unknown 9 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 18 September 2014.
All research outputs
#3,012,145
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#320
of 1,122 outputs
Outputs of similar age
#26,963
of 119,419 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#7
of 16 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,122 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 70% 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 119,419 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 77% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.