Harnessing the Power of Scientific Data Warehouses
Kevin Deeb
Data warehousing architecture should generally protect the confidentiality of data before it can be published, provide sufficient granularity to enable scientists to variously manipulate data, support robust metadata services, and define standardized spatial components. Data can then be transformed into information that would make them readily available in a common format that is easily accessible, fast, and bridges the islands of dispersed information. The benefits of the warehouse can be further enhanced by adding a spatial component so that the data can be brought to life, overlapping layers of information in a format that is easily grasped by management, enabling them to tease out trends in their areas of expertise. Full Text
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