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Unstructured Healthcare Data Archiving and Retrieval Using Hadoop and Drill

Unstructured Healthcare Data Archiving and Retrieval Using Hadoop and Drill

Hang Yue
Copyright: © 2018 |Volume: 3 |Issue: 2 |Pages: 17
ISSN: 2379-738X|EISSN: 2379-7371|EISBN13: 9781522547181|DOI: 10.4018/IJBDAH.2018070103
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MLA

Yue, Hang. "Unstructured Healthcare Data Archiving and Retrieval Using Hadoop and Drill." IJBDAH vol.3, no.2 2018: pp.28-44. http://doi.org/10.4018/IJBDAH.2018070103

APA

Yue, H. (2018). Unstructured Healthcare Data Archiving and Retrieval Using Hadoop and Drill. International Journal of Big Data and Analytics in Healthcare (IJBDAH), 3(2), 28-44. http://doi.org/10.4018/IJBDAH.2018070103

Chicago

Yue, Hang. "Unstructured Healthcare Data Archiving and Retrieval Using Hadoop and Drill," International Journal of Big Data and Analytics in Healthcare (IJBDAH) 3, no.2: 28-44. http://doi.org/10.4018/IJBDAH.2018070103

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Abstract

A healthcare hybrid Hadoop ecosystem is analyzed for unstructured healthcare data archives. This healthcare hybrid Hadoop ecosystem is composed of some components such as Pig, Hive, Sqoop and Zoopkeeper, Hadoop Distributed File System (HDFS), MapReduce and HBase. Also, Apache Drill is applied for unstructured healthcare data retrieval. This article will discuss the combination of Hadoop and Drill for data analysis applications. Based on the analysis of Hadoop components, (including HBase design) and the case studies of Drill query design regarding different unstructured healthcare data, the Hadoop ecosystem and Drill are valid tools to integrate and access voluminous complex healthcare data. They can improve the healthcare systems, achieve savings on patient care costs, optimize the healthcare supply chain and infer useful knowledge from noisy and heterogeneous healthcare data sources.

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