The MAV-ES Data Integration Approach for Decisional Information Systems (DIS): A Case on Epidemiologic Monitoring

The MAV-ES Data Integration Approach for Decisional Information Systems (DIS): A Case on Epidemiologic Monitoring

Djamila Marouf (University of Oran 1 Ahmed Benbella, Oran, Algeria), Djamila Hamdadou (University of Oran 1 Ahmed Benbella, Oran, Algeria) and Karim Bouamrane (University of Oran 1 Ahmed Benbella, Oran, Algeria)
DOI: 10.4018/IJHISI.2016100102
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Abstract

Massive data to facilitate decision making for organizations and their corporate users exist in many forms, types and formats. Importantly, the acquisition and retrieval of relevant supporting information should be timely, precise and complete. Unfortunately, due to differences in syntax and semantics, the extraction and integration of available semi-structured data from different sources often fail. Needs for seamless and effective data integration so as to access, retrieve and use information from diverse data sources cannot be overly emphasized. Moreover, information external to organizations may also often have to be sourced for the intended users through a smart data integration system. Owing to the open, dynamic and heterogeneity nature of data, data integration is becoming an increasingly complex process. A new data integration approach encapsulating mediator systems and data warehouse is proposed here. Aside from the heterogeneity of data sources, other data integration design problems include distinguishing the definition of the global schema, the mappings and query processing. In order to meet all of these challenges, the authors of this paper advocate an approach named MAV-ES, which is characterized by an architecture based on a global schema, partial schemas and a set of sources. The primary benefit of this architecture is that it combines the two basic GAV and LAV approaches so as to realize added-value benefits of the mixed approach.
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Among the determinants of health status, the health care systems and preventive care of diseases have become increasingly important. By emphasizing preventive activities, not only will there be long-term impact on the incidence of disease, but it can also lead to lowering the cost burden of disease care on society. Even so, diagnostic and therapeutic activities will alter disease prevalence and mortality; hence, these latter services are often viewed to be essential. Put together, the decision procedures for disease care are becoming increasingly complex. These decisions now require the inclusion of a growing number of considerations within increasingly advanced technological environments and must also combine a non-discriminative access to care of a constantly increasing quality to be effective.

Epidemiology is deemed essential for aiding today’s policy makers and public health professionals to think about multiple care related issues characterized by data-intensive extraction and aggregation when assessing emerging needs of public health concerns (Richard, Toubiana, Mignot et. al., 2005). Three key areas affect the complexity of epidemiological thinking. These include: (1) care provision, (2) the structuring of data collection for the care; and finally, (3) care decision making.

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