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Initial thought process:
Design a script which was simple and user friendly. Integrate procedures/functions to extract data under the hood. I focused on giving the user the option to retrieve information based on a time period, meaning using start date and end date to define a search constraint. For example, if a user wants information from 1-MAR-12 to 8-MAR-12, he or she can plug those dates in as search parameters.
Queries: I designed the queries to answer the project questions.
Scripts: pop_fact_table.sql, which means populate fact table, receives 2 parameters type, DATE, uses the dateas conditions within a select statement to create a cursor.These parameters are used to set a time period boundary. Also the order_items idsare extracted because they are unique and I used them as a condition to get theoi.movie_id, dml.distributor_id, store_id, o.total_payment, o.tax, number_of_itemsdata from multiple tables. I used order_completed_date because I felt it was importantto extract information for orders completed instead of just using the ordering date.Ordering date does not mean the order was completed. Also I fed this date intoa function which could build my time dimension table and return the time_id to insert into the facts table. Finally, the information gathered from order_items were inserted in the facts table.
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RST 4.5 is known as TRAP.
For each of the three datasets (data1.txt, data2.txt, and data3.txt in bnhw2q2.zip), cluster the data using k-means in Matlab, with k=2, 3, 4 and 5 using the provided Matlab script
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