Data cubes are mainly categorized into two categories: Multidimensional Data Cube: Most OLAP products are developed based on a structure where the cube is patterned as a multidimensional array. These multidimensional OLAP MOLAP products usually offers improved performance when compared to other approaches mainly because they can be indexed directly into the structure of the data cube to gather subsets of data. When the number of dimensions is greater, the cube becomes sparser. That means that several cells that represent particular attribute combinations will not contain any aggregated data.
This in turn boosts the storage requirements, which may reach undesirable levels at times, making the MOLAP solution untenable for huge data sets with many dimensions. The ROLAP data cube is employed as a bunch of relational tables approximately twice as many as the quantity of dimensions compared to a multidimensional array. Each one of these tables, known as a cuboid, signifies a specific view.
Share this Term. Tech moves fast! Stay ahead of the curve with Techopedia! The top level of the hierarchy is "ANY", representing any location. Western and Atlantic Canada are higher level, more general concepts than, for example, Alberta and Nova Scotia. Ordinarily, concept hierarchies are provided by a domain expert, because then the resulting general concepts will make sense to people familiar with the domain.
Concept hierarchies might also be formed automatically by clustering. To reduce the size of the data cube, we can summarize the data by computing the cube at a higher level in the concept hierarchy. A non-summarized cube would be computed at the lowest level, for example, the province level in Figure 2 a.
If we compute the cube at the second level, there are only six categories, B. Figure 3 shows a sample generalization of the Province attribute for those provinces that can be grouped under the concept Prairies and those that can be grouped under the concept Maritimes. For example, for Sask. The new, summarized concept hierarchy is shown in Figure 4.
The next step in the process is to remove duplicate tuples from the data. The tuples are removed, their individual Votes values are summed, and a new tuple replaces all three, with a Votes value of Figure 6 shows summarization of this attribute through the addition of a new category for amounts greater than or equal to This is shown in Figure 8.
Figure 9: Final Result of Generalization. Drill-down is similar to Rollup, but is in reverse. A drill-down goes from less detailed data to more detailed data. Data cubes could be sparse in many cases because not every cell in each dimension may have corresponding data in the database. If a query contains constants at even lower levels than those provided in a data cube, it is not clear how to make the best use of the precomputed results stored in the data cube. The model view data in the form of a data cube.
OLAP tools are based on the multidimensional data model. Data cubes usually model n-dimensional data. A data cube enables data to be modeled and viewed in multiple dimensions. A multidimensional data model is organized around a central theme, like sales and transactions.
A fact table represents this theme. Facts are numerical measures. Dimensions are a fact that defines a data cube. Facts are generally quantities, which are used for analyzing the relationship between dimensions. Example: In the 2-D representation , we will look at the All Electronics sales data for items sold per quarter in the city of Vancouver.
The measured display in dollars sold in thousands. Let suppose we would like to view the sales data with a third dimension. For example, suppose we would like to view the data according to time, item as well as the location for the cities Chicago, New York, Toronto, and Vancouver.
These 3-D data are shown in the table. The 3-D data of the table are represented as a series of 2-D tables. Let us suppose that we would like to view our sales data with an additional fourth dimension, such as a supplier. In data warehousing, the data cubes are n-dimensional. The cuboid which holds the lowest level of summarization is called a base cuboid.
A data cube is a multidimensional data model that store the optimized, summarized or aggregated data which eases the OLAP tools for the quick and easy analysis. Data cube stores the precomputed data and eases online analytical processing.
When it comes to cube, we, all think it as a three-dimensional structure but in data warehousing, we can implement an n-dimensional data cube. Data stored in a data cube is represented in terms of dimensions and facts. Now, what does the dimension exactly represents? The dimensions of data cube are the attitude, angle or the entities with respect to which the enterprise wants to store the data. Now, how does it help the analyst to analyze and extract the data? Let us take an example, consider we have data about AllElectronics sales.
Here we can store the sales data in many perspectives or dimensions like sales in all time, sale at all branches, sales at all location, sales of all items.
The figure below shows the data cube for AllElectronics sales. Each dimension has a dimension table which contains a further description of that dimension. A multidimensional data model like data cube is always based on a theme which is termed as fact. Like in the above example of a data set of AllElectronic we have stored data based on the sales of the electronic item.
So, here the fact is sales. A fact has a fact table associated with it.
0コメント