Problems 7–32 are based on the ER model shown in Figure P11.7. Problems 7-10 are based on the following query: SELECT P_CODE, P_PRICE FROM PRODUCT WHERE P_PRICE &gt;= (SELECT AVG(P_PRICE) FROM PRODUCT); What is the likely data sparsity of the P_PRICE column?

Database Systems: Design, Implemen...

12th Edition
Carlos Coronel + 1 other
Publisher: Cengage Learning
ISBN: 9781305627482

Chapter
Section

Database Systems: Design, Implemen...

12th Edition
Carlos Coronel + 1 other
Publisher: Cengage Learning
ISBN: 9781305627482
Chapter 11, Problem 9P
Textbook Problem
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Problems 7–32 are based on the ER model shown in Figure P11.7. Problems 7-10 are based on the following query: SELECT P_CODE, P_PRICE FROM PRODUCT WHERE P_PRICE >= (SELECT AVG(P_PRICE) FROM PRODUCT); What is the likely data sparsity of the P_PRICE column?

Program Plan Intro

Data sparsity:

The number of unique values a column could contain is referred as data sparsity. The data sparsity can be classified into two types and they are as follows:

• Low sparsity:
• When a column contains minimum number of unique values is called as low sparsity.
• Consider the column named gender; gender can contain only two different values namely ‘male’ and ‘female’ and this is considered to be a low sparsity.
• High sparsity:
• When a column contains maximum number of unique values is called as low sparsity.
• Consider the column named Birthdate; Birthdate can contain multiple different values and this is considered to be a high sparsity.

The appropriate use of index can be determined by knowing the sparsity.

Explanation of Solution

Data sparsity of the column “P_PRICE”:

• The data sparsity for the column “P_PRICE” could contain high sparsity because the col...

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