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Dimensional Modeling using Star Schema for Data Creation


Affiliations
1 Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir Srinagar, J&K, India
 

Data Warehouse design requires a to why dimensional modelling is preferred over E-R modelling when creating data warehouse. Radical rebuilding of tremendous measures of information, frequently of questionable or conflicting quality, drawn from various heterogeneous sources. Data Warehouse configuration assimilates business learning and innovation know-how. The outline of theData Warehouse requires a profound comprehension of the business forms in detail. The principle point of this exploration paper is to contemplate and investigate the transformation model to change over the E-R outlines to Star Schema for developing Data Warehouses. The Dimensional modelling is a logical design technique used for data warehouses. This research paper addresses various potential differences between the two techniques and highlights the advantages of using dimensional modelling along with disadvantages as well. Dimensional Modelling is one of the popular techniques for databases that are designed keeping in mind the queries from end-user in a data warehouse. In this paper the focus has been on Star Schema, which basically comprises of Fact table and Dimension tables. Each fact table further comprises of foreign keys of various dimensions and measures and degenerate dimensions if any. We also discuss the possibilities of deployment and acceptance of Conversion Model (CM) to provide the details of fact table and dimension tables according to the local needs. It will also highlight.

Keywords

Conversion Model (CM), E-R Modeling (ER), Dimension Modeling (DM), Fact Table, Dimensional Table, Data Warehouse (DW).
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  • Dimensional Modeling using Star Schema for Data Creation

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Authors

Mudasir M. Kirmani
Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir Srinagar, J&K, India

Abstract


Data Warehouse design requires a to why dimensional modelling is preferred over E-R modelling when creating data warehouse. Radical rebuilding of tremendous measures of information, frequently of questionable or conflicting quality, drawn from various heterogeneous sources. Data Warehouse configuration assimilates business learning and innovation know-how. The outline of theData Warehouse requires a profound comprehension of the business forms in detail. The principle point of this exploration paper is to contemplate and investigate the transformation model to change over the E-R outlines to Star Schema for developing Data Warehouses. The Dimensional modelling is a logical design technique used for data warehouses. This research paper addresses various potential differences between the two techniques and highlights the advantages of using dimensional modelling along with disadvantages as well. Dimensional Modelling is one of the popular techniques for databases that are designed keeping in mind the queries from end-user in a data warehouse. In this paper the focus has been on Star Schema, which basically comprises of Fact table and Dimension tables. Each fact table further comprises of foreign keys of various dimensions and measures and degenerate dimensions if any. We also discuss the possibilities of deployment and acceptance of Conversion Model (CM) to provide the details of fact table and dimension tables according to the local needs. It will also highlight.

Keywords


Conversion Model (CM), E-R Modeling (ER), Dimension Modeling (DM), Fact Table, Dimensional Table, Data Warehouse (DW).

References





DOI: https://doi.org/10.13005/ojcst%2F10.04.07