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What is the difference between concept description in a large database and OLAP?
Concept Description
Concept Description is a definitive type of data mining. It defines a set of data including frequent buyers, graduate candidates, etc. It describes the characterization and comparison of the data. It is also known as a class description when the concept to be described is defined as a class of objects. These descriptions can be determined with the support of data characterization.
Data characterization is a summarization of the general characteristics of the target class of data. The data relating to a specific user-defined class is usually recovered by a database query. The output of data characterization can be presented in several forms such as bar charts, curves, pie charts, and live graphs, etc.
Characterization supports a concise summarization of a given set of data, while concept or class comparison supports descriptions comparing two or more sets of data.
OLAP
OLAP represents On-Line Analytical Processing. OLAP is a categorization of software technology that empower analysts, managers, and administration to profit vision into data through quick, regular, interactive access in a large variety of possible views of data that has been changed from raw information to reflect the real dimensionality of the enterprise as accomplished by the users.
OLAP server current business users with multidimensional data from data warehouses or data marts, without concerns regarding how or where the data are saved. The physical structure and performance of OLAP servers should consider data storage issues.
Let us see the comparison between concept descriptions in large databases and OLAP tools.
| Concept description in large databases | OLAP tools |
|---|---|
| The database attributes can be of several types, such as numeric, non-numeric, spatial, text, or image. | The data warehouses and OLAP tools are established on a multidimensional data model that views the data in the form of a data cube, making attributes and measuring and constraining dimensions to non-numeric data. |
| With aggregation, concept descriptions in databases can manage complex data types of the attributes. | OLAP defines a simplified model for data analysis, because of its condition on the possible dimension and measure types. |
| Concept description in data mining needed a more automated process that supports users to decide which attributes should be included in the analysis, and the degree to which given data should be generalized to make an interesting summarization of the data. | OLAP in data warehouses is a simply user-controlled process. The selection of dimensions and the application of OLAP operations, including drill-down, roll-up, slicing, and dicing are supervised and controlled by the users. In OLAP, users are required to define a long series of OLAP operations. |
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