Hypercube in Data Warehouse and Mining

Hypercube model designing is basically a top-down approach/process. So multidimensional databases data can be represented for an application using two types of cubes i.e. hypercube and multi-cube. As shown in the figure (HYPERCUBE) below all the data appears logically as a single cube in a hypercube. All the parts of the manifold have identical dimensionality which are been represented by this hypercube.

In hypercube each dimension belongs to only one cube. A dimension is normally owned by the hypercube. Hence, this simplicity makes it easier for the users to use and understand it. As a hypercube is an top-down process, the designing of hypercube includes three major steps. The three major steps included are as follows:

1. You need to first decide which process of the business you want to capture in these model, such as sales activity.
2. Now, you identify the values which you want to be captured, such as sales amount. This information is always numeric in nature.
3. Now, identify the granularity of the data i.e. the lowest level of the data which you want to capture, this elements are dimensions. Some of the common dimensions are geography, time, customer and product. 

For example, in a retail data warehouse, a hypercube might have dimensions for product, time, and location, and a measure for sales. This would allow users to easily analyze sales data by product, time, and location, and to create reports and visualizations that show sales trends and patterns.

Hypercubes can be created using a technique called “OLAP” (Online Analytical Processing) which is a powerful tool for data mining and analysis. OLAP allows users to perform complex analytical queries on a data warehouse, such as drilling down, rolling up, and slicing and dicing data. Hypercubes are also used in data visualization tools to create interactive dashboards, charts and graphs that allow users to easily understand the data. Hypercubes can handle large amounts of data and can be used for reporting, forecasting and other data analysis tasks.

Whereas, in multi-cube model the data is been segmented into a set of smaller cubes. Each one of these is composed of a subset of an available dimensions, refer figure (multi-cube) to understand it better. These are basically used to handle multiple fact tables, each with different dimensionality. Like the hypercube model the dimensions are not always owned by any one cube, instead they are available to all cubes or there can be some dimensions which does not belong to any cube. The dimensions can be part of the multi-cubes. Hence, making it more versatile and efficient. It’s also an efficient way of storing the sparse data, also it reduces the pre-calculation database explosion effect. One of the drawback of the multi-cube is that it is less straight forward than the hypercube and can carry steeper learning curves. There are many systems which uses combination of both this approach i.e. hypercube and multi-cube. This is done by separating storage, processing and presentation layers. So it stores the data as multi-cube but presents it as hypercube.

Properties

• Multi-dimensional: Hypercubes are multi-dimensional data structures that allow for the analysis of data across multiple dimensions, such as time, geography, and product.
• Hierarchical: Hypercubes are hierarchical in nature, with each dimension being divided into multiple levels of granularity, allowing for drill-down analysis.
• Sparse: Hypercubes are sparse data structures, meaning that they only contain data for cells that have a non-zero value. This can help to reduce storage requirements.
• Pre-calculated: Hypercubes are pre-calculated, meaning that the data is already aggregated and summarized, making it easier and faster to retrieve and analyze.
• OLAP compatible: Hypercubes are typically created using OLAP, which is a powerful tool for data mining and analysis. This allows for complex analytical queries to be performed on a data warehouse.
• Data visualization: Hypercubes can be used to create interactive dashboards, charts, and graphs that make it easy for users to understand the data.
• Dynamic: Hypercubes can be dynamic in nature, allowing users to change the data dimensions and levels of granularity on the fly.
• Scalable: Hypercubes can handle large amounts of data, and can be used for reporting, forecasting, and other data analysis tasks.
• Efficient: Hypercubes are efficient in terms of storage and retrieval of data, as they only store non-zero values.
• Flexible: Hypercubes can be used for a variety of data analysis tasks, including reporting, forecasting, and data mining.

Advantages

• Ease of use: Hypercubes are simple and straightforward to use, making them easy for users to understand and navigate.
• Multi-dimensional analysis: Hypercubes allow for multi-dimensional analysis of data, which can provide more in-depth insights and understanding of trends and patterns.
• OLAP compatibility: Hypercubes can be created using OLAP, which is a powerful tool for data mining and analysis. This allows for complex analytical queries to be performed on a data warehouse.
• Data visualization: Hypercubes can be used to create interactive dashboards, charts, and graphs that make it easy for users to understand the data.
• Handling large amount of data: Hypercubes can handle large amounts of data and can be used for reporting, forecasting, and other data analysis tasks.

Disadvantages

• Complexity: Hypercubes can be complex to set up and maintain, especially for large data sets.
• Limited scalability: Hypercubes can become less efficient as the amount of data grows, which can make them less scalable.
• Performance issues: Hypercubes can experience performance issues when trying to access and analyze large amounts of data.
• Limited Flexibility: Hypercubes are designed for a specific data structure and may not be able to handle changes in the data structure or accommodate new data sources.
• Costly: Hypercubes can be costly to implement and maintain, especially for large organizations with complex data needs.