NDLI: An enhanced entity-attribute-value data model for representing high dimensional and sparse healthcare data

Content Provider	IEEE Xplore Digital Library
Author	Kamau, A. Mwangi, W.
Copyright Year	2013
Description	Author affiliation: JKUAT, Nairobi, Kenya (Kamau, A.; Mwangi, W.)
Abstract	Healthcare data are naturally sparse, high dimensional, and require frequent schema change during storage. To overcome the above challenges, both simple and multi-data-type Entity-Attribute-Value (EAV) data storage models are often used in place of the traditional horizontal data model. However, representing data in either of the formats generally result into queries that are both inefficient and rather complex. In this paper we present an enhanced data model for representing this class of data which dramatically improves the efficiency and reduces the complexity of the queries thereof. This is achieved by finding a compromise between the conventional horizontal data model and the EAV data models. To attest the validity of the new crossbreed model, Enhanced Entity-Attribute-Value (EEAV), its performance is evaluated against that of conventional horizontal data model, simple EAV data model and multi-data-type EAV data model with regard to querying of data sets of varying sizes.
Starting Page	1
Ending Page	7
File Size	154847
Page Count	7
File Format	PDF
ISBN	9781905824380
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-29
Publisher Place	Kenya
Access Restriction	Subscribed
Rights Holder	The Authors
Subject Keyword	Heart rate Healthcare data Databases High dimensional data EEAV Medical services Tin Sparse data Data models Complexity theory EAV Data mining
Content Type	Text
Resource Type	Article

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