Journal Press India®

Data Modeling Techniques for Data Warehouse

Vol 5 , Issue 2 , April - June 2017 | Pages: 51-53 | Research Paper  

https://doi.org/10.51976/ijari.521709

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Author Details ( * ) denotes Corresponding author

1. * Sona Kumra, Department of Computer Application, Bharatiyavidhyapeeth, New Delhi, Delhi, India (sonakumra1992@gmail.com)

The conceptual Entity-Relationship (ER) diagrams are extensively used for database design in relational database environment, which emphasized on day-to-day operations. Multidimensional (MD) data modelling, on the other hand, is crucial in data warehouse design, which targeted for managerial decision support. It supports decision making by allowing users to drill-down for a more detailed information, roll-up to view summarized information, slice and dice a dimension for a selection of a specific item of interest and pivot to re-orientate the view of MD data. When designing a MD model regardless whether it is a star or snowflake schema, it involves the identification of a fact, dimensions and measure attributes. This paper will explore on how the Multidimensional model can be used as the solution of data warehouse design instead of ER Model.

Keywords

Data Modeling; Data Warehouse; Multidimensional Modeling.


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  2. D Lomet, J Widom. Special Issue on Materialized Views and Data Warehousing, IEEE Data Eng in eering Bulletin 18(2), 1995.

  3. Kimball, R. The Data Warehouse Toolkit. John Wiley, 1996.

  4. Barclay TR, Barnes J, Gray P Sundaresan. Loading Databases using Dataflow Parallelism. SIGMOD Record,.23(4), 1994.

  5. Blakeley JA, N Coburn, P Larson. Updating Derived Relations: Detecting Irrelevant and Autonomously Computable Updates. ACM TODS, 4(3), 1989.

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