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    A Comparison Between Relational and Dimensional Model Techniques in a Business Intelligence Setting

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    Author
    Vacaflores, Thais
    Keyword
    data modeling
    relational
    dimensional
    Readers/Advisors
    Reale, Michael
    Jofre, Ana
    Sarner, Ronald
    Spetka, Scott
    Term and Year
    Fall 2022
    Date Published
    2022-12
    
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    URI
    http://hdl.handle.net/20.500.12648/8658
    Abstract
    Data modeling is the process of analyzing and defining the different data a business collects and produces and the relationships between that data. [8] Today there are two prevalent database models; relational modeling and dimensional modeling. These models are used to connect the various tables in a database to be used for data analysis. Most traditional businesses use a relational database to store all of their information, and when attempting to analyze data, the relational database is turned into a dimensional one. Dimensional models are considered to be simpler and better to execute queries against. Dimensional modeling is the current industry standard when analyzing data using a business intelligence tool, but are there cases when the transformation step can be skipped and a developer work with a relational model with minimal impacts on the development and user experience? This project will evaluate various aspects, such as the complexity of each model, query execution time, and time to update report elements on business intelligence tools as a result of user interaction. As well as determine if there are cases where the conversion step from a relational to a dimensional model can be skipped and queries created against the relational model instead.
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