Investigating data repair steps for EHR Big Data

Conference paper


Juddoo, S. 2022. Investigating data repair steps for EHR Big Data. 3rd International Conference on Next Generation Computing Applications (NextComp). Flic-en-Flac, Mauritius 06 - 08 Oct 2022 IEEE. https://doi.org/10.1109/nextcomp55567.2022.9932167
TypeConference paper
TitleInvestigating data repair steps for EHR Big Data
AuthorsJuddoo, S.
Abstract

This paper builds on previous research with the aim of optimizing data quality methodologies for Big Data systems, with a focus on Electronic Health Records. This optimization is performed for organisations aiming to follow a data-centric data quality strategy. One of the most important stages of a data quality lifecycle is involved with correcting dirty data detected. There is a lack of knowledge relative to the performance of existing data repair algorithms and tools in a Big Data context. This study performs a systemic review of data repair algorithms and tools, subsequently undertaking an experiment-based approach to evaluate those algorithms and tools while comparing it with a prototype built based on the results of a previous study. While some algorithms and tools could be seen to be marginally better than others, there was no algorithm or tool which was seen to be extremely adequate in the Big Data context. Thus, recommendations of improvements needed for data repair algorithms and tools for Big Data are given.

Sustainable Development Goals3 Good health and well-being
9 Industry, innovation and infrastructure
Middlesex University ThemeHealth & Wellbeing
Conference3rd International Conference on Next Generation Computing Applications (NextComp)
Proceedings Title2022 3rd International Conference on Next Generation Computing Applications (NextComp)
ISBN
Electronic9781665469548
Electronic9781665469531
Paperback9781665469555
PublisherIEEE
Publication dates
Print06 Oct 2022
Online31 Oct 2022
Publication process dates
Deposited21 Nov 2022
Accepted31 Jul 2022
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1109/nextcomp55567.2022.9932167
LanguageEnglish
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