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Danut-Octavian Simion () and Emilia Vasile ()
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Danut-Octavian Simion: Athenaeum University, Bucharest, Romania
Emilia Vasile: Athenaeum University, Bucharest, Romania

Internal Auditing and Risk Management, 2017, vol. 45, issue 1, 61-72

Abstract: The paper presents a database production model designed as a warehouse star that contain dimensions like deposits, raw materials, stocks, products, producer, locations, time and a fact table with foreign keys and measures. This model optimize the activity of a business based on a production activity in the way that it can store large amount of data in a historical way that can be the base for future scenarios with key values changed by the decision maker. The decision maker analyses a large spectrum of reports and choose what indicators to observe and what measures to display and so it’s easy to decide based on large amount of data and trends. Database applications for business improve the efficiency in managing large quantity of data in the sense for storage, updates, queries, interaction with the users and also getting answers through reports. The schema specific to a database is very flexible and permits adding or removing columns and also adding and removing entities. This feature is very useful when the relational database schema is transformed in a data warehouse shaped as a star with dimensions and a fact table. This model permits advanced queries and the usage of rollup and drill down objects specific to the business intelligence tools that offer quick responses to the complex answers. To a production business the choice of a database application designed and implemented as data warehouse star model, bennefits from all the advantage of storage and also a superior and complex tool for building queries.

Keywords: Database storage; business intelligence tools; business production model; data warehouse star model; SQL queries and reports; rollup and drill down objects (search for similar items in EconPapers)
JEL-codes: C23 C26 C38 C55 C81 C87 (search for similar items in EconPapers)
Date: 2017
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Handle: RePEc:ath:journl:v:45:y:2017:i:1:p:61-72