KC7013: Database Modelling - Mechanical Production Factories (MPF) Database System - Computer Science Assessment Answer

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Subject Code: KC7013 Internal Code: 1AGHHE

Computer Science Assessment Answer

TASK: Part 1  Scenario: Mechanical Production Factories (MPF) Database System MPF is a company that produces customised mechanical products within Europe. The company produces a rang of mechanical products at several factories. Information about which work force are assigned to which production orders and kept in the force usage register. In order to access information quickly and to ensure that all past records are available for audit purposes, the company developed a database. Figure 1 shows a UML class diagram, which provides a conceptual model of the database. Relational Design for MPF Database System A conceptual model of a database may be implemented using any database system (e.g. relational, object-relational, object-oriented). However, to start with, we have mapped the MPF’s conceptual model onto a relational logical model. Figure 2 details the relations for an implementation of the database using a relational database system. Note that Figure 2 uses shorthand / abbreviated notation for data types / domains for describing various attributes of the relations involved in the database. Computer Science Assessment TASKS 3.1 Implementing Object-Relational version of the MPF Database Implement the MPF Database using object-relational (O-R) features of Oracle 10g//11g. You need to discuss your approach to the implementation (2 marks) and provide complete code. The discussion of your approach should highlight the choices you have made during the implementation of your O-R version of the database. You need to justify your approach/choices (e.g., why object types / tables you created are appropriate for the database). Note that all relationships (e.g. one-to-one, one-to-many, many-to-many) must be bi-directional. Moreover, each to-many side of a relationship (e.g., in the case of many-to-one and many-to-many) should be implemented using nested tables. In addition, your implementation should allow all objects to be shareable (i.e. all relationships should be REF based). Oracle (2013) provides more detailed documentation on Oracle object-relational features. 3.2 Populating the O-R version of the MPF Database Using PL/SQL and/or SQL, populate your O-R version of the MPF Database with some sample data. This part can be seen to contain the following sub-tasks: (a) Creating/inserting objects in your object tables. (b) Establishing one-to-many, many-to-one, and many-to-many relationships (see the UML class diagram) among the objects created in sub-task (a).  3.3 Querying the O-R version of the MPF Database For this part, you must use PL/SQL procedures. Answer the following questions after populating the object-relational database Q a) For each of the production order, display production details, dates and hours of their workforce usage and names and ids of the associated workforce staff. Q b) For each of the product, display product details, details of the associated production orders and details of the factories where the products are produced.
  • You must submit all the SQL and PL/SQL code used for creating, populating and querying the O-R database.
  • You must submit the output of running your code (e.g., spool files).
3.4 Contrasting your O-R version with the relational version of the MPF Database  Compare and contrast the relational version of the MPF Database with your object-relational version from part 3.1. You should highlight the advantages and disadvantages of both versions. Comment on which version will best suit the MPF Database. Part 2 
  1. The Sales History (SH) Data Warehouse
SH is a sample database schema provided by Oracle, which has been extensively used in the Oracle’s Data Warehousing Guide (Lane, 2013) The SH schema, as shown in Figure 3, consists of a big fact table, SALES, and five relatively small dimension tables: TIMES, PROMOTIONS, CHANNELS, PRODUCTS and CUSTOMERS. The additional COUNTRIES table linked to CUSTOMERS creates a simple snowflake. The model and the attributes are aimed at demonstrating data warehousing functionality like star transformation and query rewrite. They do not necessarily represent the optimal approach for this kind of data warehouse in real productive environments, and such a design would be driven more by business requirements than by the star itself. Table 1 shows the cardinalities of the tables in the SH schema.
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