Reference no: EM133055846

COMP 30014 Data Warehousing - Middle East College

Learning Outcome 1: Use appropriate tools for the operation and management of a data warehouse environment.
Learning Outcome 2: Understanding and applying the data mining principles in a data warehouse.

Assignment Objective
The main objective of this assignment is to make the student develop a clear understanding of the above learning outcomes.

Assignment Tasks
Task 1

• A general overview of initial understanding of solutions to all the tasks
• What you will do with the given tasks and the dates by when they will be completed (timelines)
• An identification of Literature Resources

Task 2

Scenario and Deliverables: Implementing a Data Integration Solution for the selected organization in assignment 1.

To produce the required management report, analyze and formulate a solution for data integration. Analyze and draw relevant conclusions for the transaction database (OLTP) that contains multiple tables in the chosen entity. To integrate data into the desired management report, select two of these tables and populate by generating in high orders the required data (preferably thousands of rows). Each table must have at least 10 columns each and comprise of data types composed of integer, date, varchar2, etc. Build and use a new database with OLAP instead. To create the desired management report, you are expected to plan and build a data warehousing solution to incorporate data from the OLTP framework.

Task 3

Perform the following steps below and generate in your own terms a detailed report on how you obtained the desired solution (do not include all the screenshots, but only the final ones in each step; describe all the main steps involved in your own words briefly as well):

• Name the Project; Establish Database Connections - Create Source/Target Location.
• Design the Target Schema
• Design the necessary Extraction, Transformation, Loading (ETL) Logic.
• Deploy the Design and Execute the scenario you have chosen.

Task 4: Read the research article by Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K., & Sattar, M. (2020). Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques. Applied Sciences, 10(11), 3894. Critically discuss any five popular classifiers and consider their qualitative performance for datamining.

Attachment:- Data Warehousing.rar