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A new XML-aware compression technique for improving performance of healthcare information systems over hospital networks

Overview of attention for article published in Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society, January 2010
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Title
A new XML-aware compression technique for improving performance of healthcare information systems over hospital networks
Published in
Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society, January 2010
DOI 10.1109/iembs.2010.5626012
Pubmed ID
Authors

D Al-Shammary, I Khalil

Abstract

Most organizations exchange, collect, store and process data over the Internet. Many hospital networks deploy Web services to send and receive patient information. SOAP (Simple Object Access Protocol) is the most usable communication protocol for Web services. XML is the standard encoding language of SOAP messages. However, the major drawback of XML messages is the high network traffic caused by large overheads. In this paper, two XML-aware compressors are suggested to compress patient messages stemming from any data transactions between Web clients and servers. The proposed compression techniques are based on the XML structure concepts and use both fixed-length and Huffman encoding methods for translating the XML message tree. Experiments show that they outperform all the conventional compression methods and can save tremendous amount of network bandwidth.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 5%
United Kingdom 1 5%
Unknown 17 89%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 4 21%
Researcher 2 11%
Professor > Associate Professor 2 11%
Student > Doctoral Student 1 5%
Professor 1 5%
Other 4 21%
Unknown 5 26%
Readers by discipline Count As %
Computer Science 6 32%
Medicine and Dentistry 2 11%
Business, Management and Accounting 1 5%
Arts and Humanities 1 5%
Social Sciences 1 5%
Other 1 5%
Unknown 7 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 13 June 2012.
All research outputs
#22,764,772
of 25,377,790 outputs
Outputs from Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society
#3,438
of 4,376 outputs
Outputs of similar age
#166,064
of 172,641 outputs
Outputs of similar age from Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society
#177
of 195 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,376 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 172,641 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 195 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.