COM5013 Advanced Databases Assignment Brief | AU

Assessment specific grading criteria:

Task 1

Learning outcomes:

1.        Demonstrate a critical understanding of the tools and techniques required for advanced database development.

2.        Justify the design and development of a database application and critically evaluate the implementation and approach.

80% and above

70-79%

60-69%

50-59%

40-49%

30-39%

29% and below

Physical Model

(20%)

Extremely clear

evidence of key principles.

Very clear evidence

of key principles.

Clear evidence of

key principles.

Good evidence of

key principles.

Entities (tables), attributes (cols), relationships, cardinalities, keys and junction tables entities are present and comprehensively appropriate. Data is normalised to 3nf.

AND

Inheritance is identified and appropriate to the scenario, but missing some detail,

e.g. appropriate attributes have not been identified

for both parent and child entities.

Some evidence of

key principles.

Entities, attributes, relationships, cardinalities, keys and junction tables are present and nearly comprehensively appropriate. Data is normalised to 3nf.

AND

Parent-child entities are identified and largely appropriate for the scenario, but not be well diagrammed or completely missing attributes

AND

Some evidence of

key principles on a basic level.

Entities (tables), attributes (cols), relationships, cardinalities, keys and junction tables are present, but not consistently appropriate.

OR

Data is only partially normalised to 3nf.

OR

Inheritance example consists of inappropriate/ poorly chosen entities.

Little to no evidence of

an understanding of

ANY ONE OR MORE key

principles.

Key principles include but are not limited to: Identifying entities and attributes, normalisation of data, cardinalities, keys and inheritance.

e.g. Little to no evidence of normalisation OR cardinalities are missing OR inheritance example is missing.

OR

Completely lacks ANY ONE OF appropriate naming conventions, data types or

Entities (tables), attributes (cols), etc. as per 51-60%

Entities (tables),

attributes (cols), etc. as per 51-60%

Entities (tables),

attributes (cols), etc. as per 51-60%

AND

AND

AND

Inheritance is very well modelled and appropriate to the scenario. Appropriate constraints are identified as per 71- 80%. The overall structure is very clear and consideration has been given to factors beyond the scope of the scenario (e.g. scalability).

AND

Inheritance is well

modelled and appropriate to the scenario.

Appropriate shared/unique attributes and mandatory/ optional, overlap / disjoint constraints are also identified.

AND

All examples of advanced techniques are

Inheritance is

reasonably well modelled and appropriate to the scenario, with multiple shared/unique attributes accounted for.

AND

All examples of advanced techniques are technically appropriate, though

All examples of advanced techniques are technically appropriate and demonstrate a complex appreciation of the scenario, accounting for future extensibility.

technically appropriate and demonstrate a proper appreciation of the scenario.

they may still not all demonstrate a proper appreciation of the scenario.

AND

The examples of inheritance, de- normalisation and non-key index(es) are technically appropriate, though the 2 constraint examples may not be, and they may not demonstrate a proper appreciation of the scenario.

Includes an example of an inheritance strategy, de- normalised data, a non-key index and at least 2 of: [a default value constraint, a check constraint, an auto incrementing column]

Though any/all of these may not be particularly well considered.

OR

Does not include an example of an inheritance strategy, de- normalised data, a non-key index and at least 2 of: [a default value constraint, a check constraint, an auto incrementing column]

constraints.

OR

The structure is such that it massively limits the functionality of the database. E.g. A key one-to-many or many- to-many relationship is implemented in a one- to-one or one-to-many fashion respectively.

80% and above

70-79%

60-69%

50-59%

40-49%

30-39%

29% and below

Physical model rationale (10%)

Modelling decisions are justified in both general principles, with appropriate citations, and with reference to specific examples that may go beyond the scope of the scenario.

These must address all three of: an inheritance strategy AND denormalization AND any non-key indexes.

Modelling decisions are justified in both general principles, with appropriate citations, and with reference to scenario specific examples.

These must address all three of: an inheritance strategy AND

denormalization AND any non-key indexes.

Modelling decisions are justified in both general and scenario specific principles.

These must address all three of: an inheritance strategy AND

denormalization AND any non-key indexes.

Modelling decisions are justified using general principles.

These must address all three of: an inheritance strategy AND

denormalization AND any non-key indexes.

Modelling decisions are justified using general principles.

These must address any two of: an inheritance strategy OR denormalization OR any non-key indexes.

Lacks meaningful discussion of decision-making process.

OR

Does not include discussion of at least two of: an inheritance modelling strategy OR denormalization OR any non-key indexes.

Lacks discussion of decision-making process.

Content is largely generic, descriptive or explains terminology rather than justifying the physical model produced.

Task 2

Measures:

3.        Design and implement a database system using a range of techniques, meeting the needs of both the user and the client.

4.        Develop appropriate query language statements in order to efficiently create, manage and interrogate data within a database application.

80% and above

70-79%

60-69%

50-59%

40-49%

30-39%

29% and below

Implementation (incl. sample data) (20%)

Implementation is wholly representative of the proposed model and the execution demonstrates near professional level skills.

AND

Large amounts (~20 records per table, where appropriate), of wholly realistic data, given the scenario, is inserted for the purposes of testing using SQL scripting or columns defined/ populated by use of aggregate functions for efficiency.

Implementation is wholly representative of the proposed model, including proper implementation of advanced constraints.

AND

Moderate amounts (~ 10-20 records per table, where appropriate), of wholly realistic data, given the scenario, is inserted for the purposes of testing using SQL scripting or columns defined/ populated by use of aggregate functions for efficiency.

Implementation is wholly representative of the proposed model, including proper implementation of advanced constraints.

AND

Moderate amounts (~ 5-10 records per table, where appropriate), of semi-realistic data given the scenario, is inserted for the purposes of testing manually or using some SQL scripting or columns defined/ populated by use of aggregate functions

for efficiency.

Implementation is nearly completely representative of the proposed model.

There may be some minor deficiencies in the implementation of advanced constraints like

auto-incrementing columns.

OR

Only limited sample data sets (~1-3 records per table, where appropriate) have been inserted for testing manually.

Implementation is mostly representative of the proposed model, though there may be some minor deficiencies in execution.

e.g. Little consideration has been given to aspects like appropriate allocation of string size or numerical limits.

OR

Datasets included for testing are very limited or only partially relate to the scenario.

Implementation is partially representative of the proposed model or there are large deficiencies in the execution.

e.g. Foreign key constraints are not properly implemented.

OR

Unrealistic / extremely limited amounts of sample data inserted for testing.

Implementation is missing, barely attempted or otherwise wholly deficient.

e.g. Does not relate to the proposed model. There are grievous errors in syntax which would make the code non-functional. The code is provided but the results are not evidenced or vice- versa.

OR

Sample data is missing from one or more tables.