Abstract：
:The healthcare industry has generated large amounts of data, and analyzing these has emerged as an important problem in recent years. The MapReduce programming model has been successfully used for big data analytics. However, data skew invariably occurs in big data analytics and seriously affects efficiency. To overcome the data skew problem in MapReduce, we have in the past proposed a data processing algorithm called Partition Tuning-based Skew Handling (PTSH). In comparison with the one-stage partitioning strategy used in the traditional MapReduce model, PTSH uses a two-stage strategy and the partition tuning method to disperse key-value pairs in virtual partitions and recombines each partition in case of data skew. The robustness and efficiency of the proposed algorithm were tested on a wide variety of simulated datasets and real healthcare datasets. The results showed that PTSH algorithm can handle data skew in MapReduce efficiently and improve the performance of MapReduce jobs in comparison with the native Hadoop, Closer, and locality-aware and fairness-aware key partitioning (LEEN). We also found that the time needed for rule extraction can be reduced significantly by adopting the PTSH algorithm, since it is more suitable for association rule mining (ARM) on healthcare data.
journal_name
J Healthc Engjournal_title
Journal of healthcare engineeringauthors
Gao Y,Zhou Y,Zhou B,Shi L,Zhang Jdoi
10.1155/2017/1425102subject
Has Abstractpub_date
2017-01-01 00:00:00eissn
2040-2295issn
2040-2309journal_volume
2017pub_type
杂志文章abstract：:Diabetic skin manifestations, previous to ulcers and wounds, are not highly accounted as part of diagnosis even when they represent the first symptom of vascular damage and are present in up to 70% of patients with diabetes mellitus type II. Here, an application for skin macules characterization based on a three-stage...
journal_title：Journal of healthcare engineering
pub_type： 杂志文章
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
pub_type： 杂志文章
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
pub_type： 杂志文章
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
pub_type： 杂志文章
doi：10.1260/2040-2295.5.3.313
更新日期：2014-01-01 00:00:00
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journal_title：Journal of healthcare engineering
pub_type： 杂志文章
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更新日期：2019-02-24 00:00:00
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journal_title：Journal of healthcare engineering
pub_type： 杂志文章,评审
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更新日期：2019-06-16 00:00:00
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
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更新日期：2018-02-25 00:00:00
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journal_title：Journal of healthcare engineering
pub_type： 杂志文章
doi：10.1155/2016/1245418
更新日期：2016-01-01 00:00:00
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journal_title：Journal of healthcare engineering
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更新日期：2018-04-01 00:00:00
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
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journal_title：Journal of healthcare engineering
pub_type： 杂志文章
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pub_type： 杂志文章
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