ArticleIEEE journal of biomedical and health informatics2016
Efficient Execution Methods of Pivoting for Bulk Extraction of Entity-Attribute-Value-Modeled Data.
Article in IEEE journal of biomedical and health informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Big data management challenges in health research-a literature review.Briefings in bioinformatics · 2019Review
- Using i2b2 to Bootstrap Rural Health Analytics and Learning Networks.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2016Article
- PredicT-ML: a tool for automating machine learning model building with big clinical data.Health information science and systems · 2016Article
- MLBCD: a machine learning tool for big clinical data.Health information science and systems · 2015Article
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2 authors.
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Abstract
Entity-attribute-value (EAV) tables are widely used to store data in electronic medical records and clinical study data management systems. Before they can be used by various analytical (e.g., data mining and machine learning) programs, EAV-modeled data usually must be transformed into conventional relational table format through pivot operations. This time-consuming and resource-intensive process is often performed repeatedly on a regular basis, e.g., to provide a daily refresh of the content in a clinical data warehouse. Thus, it would be beneficial to make pivot operations as efficient as possible. In this paper, we present three techniques for improving the efficiency of pivot operations: 1) filtering out EAV tuples related to unneeded clinical parameters early on; 2) supporting pivoting across multiple EAV tables; and 3) conducting multi-query optimization. We demonstrate the effectiveness of our techniques through implementation. We show that our optimized execution method of pivoting using these techniques significantly outperforms the current basic execution method of pivoting. Our techniques can be used to build a data extraction tool to simplify the specification of and improve the efficiency of extracting data from the EAV tables in electronic medical records and clinical study data management systems.
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