IFB105 – Assignment 1 – Answer ExamplePage 1TASK 1 (15 Marks)Your task is to design an information (data) model that suggests an effective and efficient way for storingdata of a Cruise Club working in the health area. As you are an expert in ORM, you decide to develop anORM model.Complete the drawing of the ORM conceptual schema for the universe of discourse as specified in the formbelow by performing steps 1-5 as described below of the Conceptual Schema Design Procedure.Note: The following steps 1 – 5 can be done other ways as well. Refer to textbook and/or shortpresentation slides/videos for other examples.Step 1: Transform familiar examples into elementary facts and apply quality checks.List all the deep structure sentences that you can identify based on the familiar examples mentioned in thescenario.Example answers:• The Member with MemberNr ‘33’ has Password ‘password’.• The Member with MemberNr ‘33’ approved the Motion with MotionNr ‘52’.• The Member with MemberNr ‘33’ rejected the Motion with MotionNr ’53’.IFB105 – Assignment 1 – Answer ExamplePage 2• The Motion with MotionNr ‘52’ has the MotionText ‘Ban smoking in restaurant’.Step 2: Draw the fact types and apply a population check.Use the deep structure sentences from Step 1 to draw fact types. As an example, consider the only binaryfact type shown in the diagram above. Note that this fact type is introduced based on the example deepstructure sentence proposed in the description of Step 1.Perform a population check by populating identified fact types with the fact instances captured in the deepstructure sentences. This can be accomplished by drawing fact tables.For all the identified fact types discuss/demonstrate that they are indeed elementary by performing all thenecessary split and join operations on sample populationsExample answer:All relationships illustrated in the above figure should be binary relationships, after checked with populationcheck. One example population check (i.e. split-and-join operation) is demonstrated in this report.Suppose member, motion and motion text are included in a ternary fact type in terms of “approved”relationship. The population check process is illustrated below.The original table: MemberApproved MotionMotion Text3353Change ship name to “Titanic”3452Ban smoking in restaurant Splitting the above table yields the two following tables. MemberApproved Motion IFB105 – Assignment 1 – Answer ExamplePage 3 33533452 Approved MotionMotion Text53Change ship name to “Titanic”52Ban smoking in restaurant Joining the above two tables then produces the below joined table: MemberApproved MotionMotion Text3353Change ship name to “Titanic”3452Ban smoking in restaurant As the joined table has no different data from the original table, the supposed ternary fact including member,motion, and motion text is incorrect. There should be two binary relationships among the three entities/valuetype.Step 3: Check for entity types to be combined and note any arithmetic derivations.Discuss in text if it does or does not make sense to combine any of the entity types proposed in the diagram.In this discussion do not exceed the word limit of 300 words.Introduce TWO arithmetically derivable fact types in your ORM model. You can use mathematical notationor textual description to specify the derivation rules.Example answer:As shown in the model, none of the face type is of the same unit-based reference mode or of the samecategory. Therefore, none of the entities can be combined to a certain primitive type.Additionally, no significant arithmetic relationships can be found in the proposed ORM model.Step 4: Add uniqueness constraints and check the arity (length) of fact types.Introduce all the uniqueness constraints in your ORM model that you can identify based on the scenario.For each introduced uniqueness constraint, briefly explain the rationale behind your decision to include it inthe model.Example answer:IFB105 – Assignment 1 – Answer ExamplePage 4Explanations for the identifies UC:• For each member, there is only one password associated with that member. Therefore, member andpassword have a one-to-many relationship.• Each member can approve multiple motions and each motion can be approved by multiple members.Therefore, member and motion have a many-to-many relationship in terms of “approved”relationship.• Each member can reject multiple motions and each motion can be rejected by multiple members.Therefore, member and motion have a many-to-many relationship in terms of “rejected” relationship.• Each motion has only one motion text, and each motion text belongs to only one motion. Therefore,motion and motion text have a one-to-one relationship.Step 5: Add mandatory role constraints, and check for logical derivations.Introduce all the mandatory role constraints in your ORM model that you can identify based on the scenario.For each introduced mandatory role constraint, briefly explain the rationale behind your decision to includeit in the model.IFB105 – Assignment 1 – Answer ExamplePage 5Explanations for the identifies mandatory constraints:• Each member must have a password. Therefore, member is mandatory on password.• Some members may not approve any motion. Therefore, member is optional on motion in terms ofthe “approved” relationship.• Some members may not reject any motion. Therefore, member is optional on motion in terms of the“rejected” relationship.• Some motions may not be approved by any member. Therefore, motion is optional on member interms of the “approved” relationship.• Some motions may not be rejected by any member. Therefore, motion is optional on member interms of the “rejected” relationship.• Each motion must have a motion text. Therefore, member is mandatory on motion text.In the proposed ORM model, no significant logical derivations can be found.
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