Construct a bitmap index on the attribute salary, dividing salary values into 2 ranges: below 40000, 40000 to below 60000.
Lacking data.
To construct a bitmap index on the attribute "salary" with two ranges (below 40000 and 40000 to below 60000), follow these steps:
Step 1: Start with a table containing the "salary" attribute.
Step 2: Create two bitmap index structures, one for each range.
Step 3: For the "below 40000" range, assign a bitmap index to each salary value that falls below 40000. Set the corresponding bit for each record that satisfies this condition.
Step 4: For the "40000 to below 60000" range, assign a bitmap index to each salary value that falls between 40000 (inclusive) and 60000 (exclusive). Set the corresponding bit for each record that satisfies this condition.
Step 5: Each bitmap index should have one bit per record in the table and represents the presence or absence of a record in a specific range. Set the bit to 1 if the record satisfies the condition for that range, and set it to 0 if it does not.
Step 6: Once all the bits are set for each range, the bitmap index is complete.
Using this bitmap index, you can quickly identify which records fall into each range by performing bitwise operations on the corresponding bitmaps.
To construct a bitmap index on the "salary" attribute, dividing the salary values into two ranges - below 40000 and 40000 to below 60000 - you can follow these steps:
1. Start by creating two bitmap index structures, one for each range.
2. Initialize the bitmap index structures with a bitmap for each distinct value within the ranges.
3. Scan the dataset and for each salary value, determine which range it falls into (below 40000 or 40000 to below 60000).
4. For each salary value below 40000, set the corresponding bit in the first bitmap index structure to 1.
5. For each salary value between 40000 (inclusive) and 60000 (exclusive), set the corresponding bit in the second bitmap index structure to 1.
6. Once you have scanned the entire dataset, the bitmap index structures now represent the presence or absence of each salary value within the defined ranges.
7. Optimize the bitmap index structures by compressing them. This can reduce the memory footprint and improve query performance.
8. Finally, update the metadata associated with the bitmap index to reflect the defined ranges and their corresponding bitmap index structures.
By constructing this bitmap index, you can efficiently query the dataset based on salary ranges. For example, if you want to find all records with salaries below 40000, you can simply perform a bitwise AND operation on the first bitmap index structure and retrieve the corresponding records. Similarly, you can do the same for salaries within the range of 40000 to below 60000 using the second bitmap index structure.