An Iceberg Cube is a data cube that contains only the cells satisfying a specific aggregate condition, effectively representing the "tip of the iceberg" of potentially much larger data.
Understanding Iceberg Cubes
Iceberg cubes are a technique used in data warehousing and OLAP (Online Analytical Processing) to efficiently analyze large datasets. Instead of materializing the entire data cube, which can be computationally expensive and resource-intensive, an iceberg cube focuses on the most significant data points.
Aggregate Condition
The defining characteristic of an iceberg cube is the aggregate condition. This condition specifies a minimum threshold that a cell's aggregate value must meet to be included in the cube. Common aggregate conditions include:
- Minimum Support: The cell must have a minimum number of occurrences in the underlying data. This is frequently used in association rule mining.
- Lower Bound on Average: The average value for the cell must be above a certain threshold.
- Minimum/Maximum Value: The cell's minimum or maximum aggregate value must meet a certain criterion.
Why Use Iceberg Cubes?
- Efficiency: By materializing only the significant cells, iceberg cubes reduce the computational and storage requirements.
- Focus on Important Data: They help analysts focus on the most relevant information by filtering out less significant data points.
- Scalability: Iceberg cubes are more scalable than full data cubes, especially for large datasets.
Example
Imagine a data cube that tracks sales data by product, region, and time. Instead of materializing every possible combination, we can create an iceberg cube with a minimum sales threshold. Only product-region-time combinations that exceed this sales threshold would be included in the iceberg cube. For instance, only regions and products with sales exceeding $10,000 within a given month are retained.
Construction of Iceberg Cubes
Several algorithms and techniques exist for efficiently constructing iceberg cubes, often involving pruning techniques and specialized data structures to minimize the computational overhead.
