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Problems 15 and 16 are based on the following query: SELECT P_CODE, P_QOH*P_PRICE FROM PRODUCT WHERE P_QOH*P_PRICE > (SELECT AVG(P_QOH*P_PRICE) FROM PRODUCT); What is the likely data sparsity of the P_QOH and P_PRICE columns?
Database Systems: Design, Implemen...
Solutions
Database Systems: Design, Implemen...
Problems 15 and 16 are based on the following query:
| SELECT | P_CODE, P_QOH*P_PRICE |
| FROM | PRODUCT |
| WHERE | P_QOH*P_PRICE > (SELECT AVG(P_QOH*P_PRICE) FROM PRODUCT); |
What is the likely data sparsity of the P_QOH and P_PRICE columns?
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 “P_QOH” and “P_PRICE”:
- The data sparsity for the attributes “P_QOH” and “P_PRICE” is, it could contain high sparsity because the column in the PROD...
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