CLINICAL DATA WAREHOUSE: A REVIEW

Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW.

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A Survey of Data Warehouse Model Evolution

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch015 ◽

2009 ◽

pp. 129-136

Author(s):

Cécile Favre ◽

Fadila Bentayeb ◽

Omar Boussaid

Keyword(s):

Data Warehouse ◽

Data Warehousing ◽

Business Environment ◽

Heterogeneous Data ◽

Data Sources ◽

Future Trends ◽

Model Evolution ◽

Heterogeneous Data Sources ◽

Key Points ◽

Existing Data

A data warehouse allows the integration of heterogeneous data sources for analysis purposes. One of the key points for the success of the data warehousing process is the design of the model according to the available data sources and the analysis needs (Nabli, Soussi, Feki, Ben-Abdallah & Gargouri, 2005). However, as the business environment evolves, several changes in the content and structure of the underlying data sources may occur. In addition to these changes, analysis needs may also evolve, requiring an adaptation to the existing data warehouse’s model. In this chapter, we provide an overall view of the state of the art in data warehouse model evolution. We present a set of comparison criteria and compare the various works. Moreover, we discuss the future trends in data warehouse model evolution.

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Domain/Mapping Model: A Novel Data Warehouse Data Mode

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2017.2.2876 ◽

2017 ◽

Vol 12 (2) ◽

pp. 166 ◽

Cited By ~ 2

Author(s):

Ivan Bojicic ◽

Zoran Marjanovic ◽

Nina Turajlic ◽

Marko Petrovic ◽

Milica Vuckovic ◽

...

Keyword(s):

Data Warehouse ◽

Data Model ◽

Heterogeneous Data ◽

Physical Structure ◽

Data Sources ◽

Mapping Model ◽

Heterogeneous Data Sources ◽

Domain Mapping ◽

Source Models ◽

Data Source

In order for a data warehouse to be able to adequately fulfill its integrative and historical purpose, its data model must enable the appropriate and consistent representation of the different states of a system. In effect, a DW data model, representing the physical structure of the DW, must be general enough, to be able to consume data from heterogeneous data sources and reconcile the semantic differences of the data source models, and, at the same time, be resilient to the constant changes in the structure of the data sources. One of the main problems related to DW development is the absence of a standardized DW data model. In this paper a comparative analysis of the four most prominent DW data models (namely the relational/normalized model, data vault model, anchor model and dimensional model) will be given. On the basis of the results of [1]a, the new DW data model (the Domain/Mapping model- DMM) which would more adequately fulfill the posed requirements is presented.

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Temporal Semistructured Data Models and Data Warehouses

Data Warehouses and OLAP ◽

10.4018/987-1-59904-364-7.ch012 ◽

2011 ◽

pp. 277-297 ◽

Cited By ~ 2

Author(s):

Carlo Combi ◽

Barbara Oliboni

Keyword(s):

Data Warehouse ◽

Data Model ◽

Heterogeneous Data ◽

Data Models ◽

Semistructured Data ◽

Data Sources ◽

Time Varying ◽

Data Warehouses ◽

Time Dimension ◽

Heterogeneous Data Sources

This chapter describes a graph-based approach to represent information stored in a data warehouse, by means of a temporal semistructured data model. We consider issues related to the representation of semistructured data warehouses, and discuss the set of constraints needed to manage in a correct way the warehouse time, i.e. the time dimension considered storing data in the data warehouse itself. We use a temporal semistructured data model because a data warehouse can contain data coming from different and heterogeneous data sources. This means that data stored in a data warehouse are semistructured in nature, i.e. in different documents the same information can be represented in different ways, and moreover, the document schemata can be available or not. Moreover, information stored into a data warehouse is often time varying, thus as for semistructured data, also in the data warehouse context, it could be useful to consider time.

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