Dimensional Modeling using Star Schema for Data Creation
نویسنده
چکیده
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 Oriental Journal of Computer Science and Technology Journal Website: www.computerscijournal.org ISSN: 0974-6471, Vol. 10, No. (4) 2017, Pg. 745-754
منابع مشابه
The Comparison of Anchor and Star Schema from a Query Performance Perspective
Today's business environment requires that companies have access to highly relevant information in a matter of seconds. Modern Business Intelligence tools rely on data structured mostly in traditional dimensional database schemas, typically represented by star schemas. Dimensional modeling is already recognized as a leading industry standard in the field of data warehousing although several dra...
متن کاملAn Analysis of Many-to-Many Relationships Between Fact and Dimension Tables in Dimensional Modeling
Star schema, which maintains one-to-many relationships between dimensions and a fact table, is widely accepted as the most viable data representation for dimensional analysis. Realworld DW schema, however, frequently includes many-to-many relationships between a dimension and a fact table. Having those relationships in a dimensional model causes several difficult issues, such as losing the simp...
متن کاملThe Design of Multidimensional Data Model Using Principles of the Anchor Data Modeling: An Assessment of Experimental Approach Based on Query Execution Performance
The decision making processes need to reflect changes in the business world in a multidimensional way. This includes also similar way of viewing the data for carrying out key decisions that ensure competitiveness of the business. In this paper we focus on the Business Intelligence system as a main toolset that helps in carrying out complex decisions and which requires multidimensional view of d...
متن کاملEvolution of a Dynamic Multidimensional Denormalization Meta Model Using Object Role Modeling
At Guidant, a Boston Scientific Company, systems that collect data in support of medical device clinical research trials must be capable of collecting large, dynamic sets of attributes that are often reused in later research activities. Their resultant design, based on conceptual analysis using Object Role Modeling (ORM), transforms each unique business fact into an instance in a highly normali...
متن کاملFrom enterprise models to dimensional models: a methodology for data warehouse and data mart design
This paper describes a method for developing dimensional models from traditional Entity Relationship models. This can be used to design data warehouses and data marts based on enterprise data models. The first step of the method involves classifying entities in the data model into a number of categories. The second step involves identifying hierarchies that exist in the model. The final step in...
متن کامل