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Assignment 601.

Instructions on Assessment:

Mapping to Programme Goals and Objectives

This assignment covers the following learning outcomes for the module:

Knowledge & Understanding:

2. Demonstrate deep knowledge of key concepts of data warehousing, data analytics, data standards, and data quality

Intellectual / Professional skills & abilities:

4. Appraise, analyse, design, develop and evaluate data warehousing and data analytics solutions using Oracle database system

Personal Values Attributes (Global / Cultural awareness,  Ethics, Curiosity) (PVA):

5. Develop critical awareness of the responsibilities of database developer with respect to professional, legal, security and ethical issues individually or as part of a team

Assessment Regulations

You are advised to read the guidance for students regarding assessment policies (Northumbria, 2019). They are available online here.

Late submission of work

Where coursework is submitted late without approval, after the published hand-in deadline, the following penalties will apply.

For coursework submitted up to 1 working day (24 hours) after the published hand-in deadline without approval, 10% of the total marks available for the assessment (i.e.100%) shall be deducted from the assessment mark.

Coursework submitted more than 1 working day (24 hours) after the published hand-in deadline without approval will be regarded as not having been completed. A mark of zero will be awarded for the assessment and the module will be failed, irrespective of the overall module mark.

These provisions apply to all assessments, including those assessed on a Pass/Fail basis.

The full policy can be found here.

Students must retain an electronic copy of this assignment (including ALL appendices) and it must be made available within 24hours of them requesting it be re-submitted.

Academic Misconduct

The Assessment Regulations for Taught Awards (ARTA) contain the Regulations and procedures applying to cheating, plagiarism and other forms of academic misconduct.

The full policy is available at here

You are reminded that plagiarism, collusion and other forms of academic misconduct as referred to in the Academic Misconduct procedure of the assessment regulations, which are taken very seriously. Assignments in which evidence of plagiarism or other forms of academic misconduct is found may receive a mark of zero.

Criteria for success:

For textual components :

80-100% – The description will excellently cover all the specific topics requested. The written work will be fluent, clearly presented and of out-standing quality.

70-79% – The description will comprehensively cover all the specific topics requested. The written work will be fluent and clearly presented and of distinctive quality.

60-69% – The student will show a very good knowledge of the specific topics, with very good presentation skills and quality.

50-59% – The student will show an above average knowledge of the specific topics, with above average presentation skills and quality.

40-49% – There will be an inadequate description of a significant proportion of the topics requested. There will be no major failures in presentation clarity though partly inadequate.

Less than 40% – There will be little or no information conveyed in an intelligible manner on the specific topics requested.

(e.g., following sound algorithms, standards, methods, error free SQL code),

For SQL and other database technical components:

80-100% – The students will produce exceptional models and solutions, and will demonstrate the use of notation/language, which have outstanding syntactic accuracy (e.g., following sound algorithms, standards, methods, error free SQL code) with exceptional semantic relevance (e.g., are relevant to the requirements of the particular scenario).

70-79% – The students will produce fully complete models and solutions, and will demonstrate the use of notation/language, which have high syntactic accuracy, with high semantic relevance.

60-69% – The students will produce almost models and solutions, and will demonstrate the use of notation/language, which have appropriate syntactic accuracy with reasonably well semantic relevance.

50-59% – The students will produce fairly complete models and solutions, and will demonstrate the use of notation/language, which have adequate syntactic accuracy with reasonable semantic relevance.

40-49% – The students will produce models and solutions, and will demonstrate the use of notation/language, which have some syntactic accuracy and semantic relevance but on balance inadequate as a whole.

Less than 40% – The students will not produce sufficient models and solutions, and/or will be unable to demonstrate the use of notation/language with significant syntactic accuracy and/or significant semantic relevance.

Assessment Background and Scenario

This assessment is based on two scenarios as follows:

  1. The Sales History (SH) Data Warehouse scenario. SH is a sample database schema provided by Oracle, which has been extensively used in the Oracle’s Data Warehousing Guide (Lane, 2013). The details of this scenario are provided in Appendix 1.

Assignment Questions

Part 1:  Data Warehousing Tasks (50 Marks)

This part is based on the Sales History scenario as described in Appendix 1.

You must submit all the SQL queries and any other code that you wrote in answering any of the tasks / questions (e.g., the use of Explain Plan statements for the queries and their outputs using Spooling or other suitable means).

(9 marks)

(9 marks)

(8 marks)

(12 marks)

  1. Using CUBE, write an SQL query over the SH schema under your DWU account involving one fact table (SALES or COSTS) and at least twodimension tables and at least 3 grouping attributes. Provide output of successful execution of your query. Provide reasons why your query may be useful for users of the SH data warehouse.

(3 marks)

  1. Using set operation UNION ALL (and not CUBE), write an SQL query that produces the same result as the query in (a) above. Provide output of successful execution of your query.

(5 marks)

  1. Using EXPLAIN PLAN, provide a detailed discussion analysing costs of evaluating the above queries (i.e. with and without ROLLUP).

(4 marks)

Part 2:  Data Mining Tasks (35 Marks)

This part is based on the UniTel scenario as described in Appendix 2. Moreover, you must use the DMUn Oracle Data Mining Account (where 1 <= n <= 75, e.g., DMU1, DMU2) allocated to your group.

Jessica is the customers relation manager at UniTel. She wants to know the possibility of potential churn of the company’s customers based on previous experience, so she may be able take some actions accordingly to retain their customers.

To help Jessica in doing her analysis, we need to investigate what could be a suitable algorithm for solving her problem. The data from last year are used as the training data and the data of February of this year are taken as the testing data to verify the model accuracy. Data of all the columns are used to set up the model. To meet the requirement, many algorithms can be selected.

Oracle Data Mining (ODM) provides the following algorithms for classification:

Assignment 601

 
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