An online analytical processing multi-dimensional data warehouse for malaria data

Arifin, S.M. Niaz, Madey, Gregory R., Vyushkov, Alexander, Raybaud, Benoit, Burkot, Thomas R., and Collins, Frank H. (2017) An online analytical processing multi-dimensional data warehouse for malaria data. Database: the journal of biological databases and curation, 2017. bax073.

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Malaria is a vector-borne disease that contributes substantially to the global burden of morbidity and mortality. The management of malaria-related data from heterogeneous, autonomous, and distributed data sources poses unique challenges and requirements. Although online data storage systems exist that address specific malaria-related issues, a globally integrated online resource to address different aspects of the disease does not exist. In this article, we describe the design, implementation, and applications of a multidimensional, online analytical processing data warehouse, named the VecNet Data Warehouse (VecNet-DW). It is the first online, globally-integrated platform that provides efficient search, retrieval and visualization of historical, predictive, and static malaria-related data, organized in data marts. Historical and static data are modelled using star schemas, while predictive data are modelled using a snowflake schema. The major goals, characteristics, and components of the DW are described along with its data taxonomy and ontology, the external data storage systems and the logical modelling and physical design phases. Results are presented as screenshots of a Dimensional Data browser, a Lookup Tables browser, and a Results Viewer interface. The power of the DW emerges from integrated querying of the different data marts and structuring those queries to the desired dimensions, enabling users to search, view, analyse, and store large volumes of aggregated data, and responding better to the increasing demands of users.

Item ID: 51617
Item Type: Article (Research - C1)
ISSN: 1758-0463
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© The Author(s) 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Funders: Bill and Melinda Gates Foundation (BMGF)
Projects and Grants: BMGF grant no. 1030706-302365, BMFG grant no. 18931
Date Deposited: 22 Nov 2017 07:46
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3207 Medical microbiology > 320704 Medical parasitology @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970111 Expanding Knowledge in the Medical and Health Sciences @ 50%
92 HEALTH > 9202 Health and Support Services > 920299 Health and Support Services not elsewhere classified @ 50%
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