Data Mining_ Concepts and Techniques - Jiawei Han [15]
A relational database for AllElectronics
The fictitious AllElectronics store is used to illustrate concepts throughout this book. The company is described by the following relation tables: customer, item, employee, and branch. The headers of the tables described here are shown in Figure 1.5. (A header is also called the schema of a relation.)
Figure 1.5 Relational schema for a relational database, AllElectronics.
■ The relation customer consists of a set of attributes describing the customer information, including a unique customer identity number (cust_.5ptID), customer name, address, age, occupation, annual income, credit information, and category.
■ Similarly, each of the relations item, employee, and branch consists of a set of attributes describing the properties of these entities.
■ Tables can also be used to represent the relationships between or among multiple entities. In our example, these include purchases (customer purchases items, creating a sales transaction handled by an employee), items_sold (lists items sold in a given transaction), and works_at (employee works at a branch of AllElectronics).
Relational data can be accessed by database queries written in a relational query language (e.g., SQL) or with the assistance of graphical user interfaces. A given query is transformed into a set of relational operations, such as join, selection, and projection, and is then optimized for efficient processing. A query allows retrieval of specified subsets of the data. Suppose that your job is to analyze the AllElectronics data. Through the use of relational queries, you can ask things like, “Show me a list of all items that were sold in the last quarter.” Relational languages also use aggregate functions such as sum, avg (average), count, max (maximum), and min (minimum). Using aggregates allows you to ask: “Show me the total sales of the last month, grouped by branch,” or “How many sales transactions occurred in the month of December?” or “Which salesperson had the highest sales?”
When mining relational databases, we can go further by searching for trends or data patterns. For example, data mining systems can analyze customer data to predict the credit risk of new customers based on their income, age, and previous credit information. Data mining systems may also detect deviations—that is, items with sales that are far from those expected in comparison with the previous year. Such deviations can then be further investigated. For example, data mining may discover that there has been a change in packaging of an item or a significant increase in price.
Relational databases are one of the most commonly available and richest information repositories, and thus they are a major data form in the study of data mining.
1.3.2. Data Warehouses
Suppose that AllElectronics is a successful international company with branches around the world. Each branch has its own set of databases. The president of AllElectronics has asked you to provide an analysis of the company's sales per item type per branch for the third quarter. This is a difficult task, particularly since the relevant data are spread out over several databases physically located at numerous sites.
If AllElectronics had a data warehouse, this task would be easy. A data warehouse is a repository of information collected from multiple sources, stored under a unified schema, and usually residing at a single site. Data warehouses are constructed via a process of data cleaning, data integration, data transformation, data loading, and periodic data refreshing. This process is discussed in Chapter 3 and Chapter 4. Figure 1.6 shows the typical framework for construction and use of a data warehouse for AllElectronics.
Figure 1.6 Typical framework of a data warehouse for AllElectronics.
To facilitate decision making, the data in a data warehouse are organized around major subjects (e.g., customer, item, supplier, and activity). The data