Information Integration

Transcription

1 Chapter 11 Information Integration While there are many directions in which modern database systems are evolving, a large family of new applications fall undei the general heading of information integration. Such applications take data that is stored in two or more databases (information sources) and build from them one large database, possibly virtual, containing information from all the sources, so the data can be queried as a unit. The sources may be conventional databases or other types of information, such as collections of Web pages. In this chapter, we shall introduce important aspects of information integration. We begin with an outline of the principal approaches to integration: federation, warehousing, and mediation. A specialized database architecture, called the "data cube," is introduced as a way to organize the integrated data in some applications. Our study also covers specialized applications that have grown around our ability to integrate information: "OLAP" (on-line analytic processing) and "data mining." 11.1 Modes of Information Integration There are several ways that databases or other, possibly distributed, information sources can be made to work together. In this section, we consider the three most common approaches: 1. Federated databases. The sources are independent, but one source can call on others to supply information. 2. Warehousing. Copies of data from several sources are stoied in a single database, called a (data) warehouse. Possibly, the data stored at the warehouse is first processed in some way before storage; e.g., data may be filtered, and relations may be joined or aggregated. The warehouse is updated periodically, perhaps overnight. As the data is copied from the sources, it may need to be transformed in certain ways to make all data conform to the schema at the warehouse. 595

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4 598 CHAPTER 11. INFORMATION INTEGRATION

5 11.1. MODES OF INFORMATION INTEGRATION 599

6 600 CHAPTER 11. INFORMATION INTEGRATION

7 11.1. MODES OF INFORMATION INTEGRATION 601

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9 11.1. MODES OF INFORMATION INTEGRATION 603 would typically be more than two sources. To begin, the user issues a query to the mediator. Since the mediator has no data of its own, it must get the

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11 11.2. WRAPPERS IN MEDIATOR-BASED SYSTEMS 605 a) If company A wants to insert into its relations information about the corresponding items from B, what SQL insert statements should it use?

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13 11.2. WRAPPERS IN MEDIATOR-BASED SYSTEMS 607 SELECT * FROM AutosMed WHERE color = '$<:'; => SELECT serialno, model, color, autotrans, 'dealer1' FROM Cars WHERE color = '$c';

14 608 CHAPTER 11. INFORMATION INTEGRATION

15 11.2. WRAPPERS IN MEDIATOR-BASED SYSTEMS 609

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17 11.2. WRAPPERS IN MEDIATOR-CASED SYSTEMS 611 Figure 11.9: Query from mediator to wrapper RedAutos(serialNo,

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21 11.3. ON-LINE ANALYTIC PROCESSING 615

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23 11.3. ON-LINE ANALYTIC PROCESSING 617

24 618 CHAPTER 11. INFORMATION INTEGRATION

25 11.3. ON-LINE ANALYTIC PROCESSING 619 Figure 11.15: Selecting a slice of a diced cube SELECT grouping attributes and aggregations FROM fact table joined with zero or more dimension tables WHERE certain attributes

26 620 CHAPTER 11. INFORMATION INTEGRATION The ability to use such a "relation" is one way that a data-cube system is a specialization of a DBMS. We might discover that red Gobis have not sold well recently. The next question we might ask is whether this problem exists at all dealers, or whether only some dealers have had low sales of red Gobis. Thus, we further focus the query by looking at only red Gobis, and we partition along the dealer dimension

27 11.4. DATA CUBES 621 Drill-Down and Roll-Up

28 622 CHAPTER 11. INFORMATION INTEGRATION The Cube Operator Given a fact table F, we can define an augmented table CUBE(F) that adds

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32 626 CHAPTER 11. INFORMATION INTEGRATION becomes increasingly expensive to store the results of aggregating by every possible combination of groupings. Not only are there too many of them, but

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36 630 CHAPTER 11. INFORMATION INTEGRATION we observed in the original example, Q\ can be answered from either SalesVl or SalesV2; of course it could also be answered fiom the full data cube Sales, but there is no reason to want to do so if one of the other views is materialized. Q% can be answered from either SalesVl 01 Sales, while Q$ can only be answered from Sales. Each of these relationships is expressed in Fig by the paths downward from the queries to theii supporting views. D

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39 11.5. DATA MINING 633 Data-Mining Queries and Decision-Support Queries While decision-support queries may need to examine and aggregate large portions of the data in a database, the analyst posing the query usually tells the system exactly what query to execute; i.e., on which portion of the data to focus. A data-mining query goes a step beyond, inviting the system to decide where the focus should be. For example, a decisionsupport query might ask to "aggregate the sales of Aardvark automobiles by color and year," while a data-mining query might ask "what are the factors that have had the most influence ovei Aardvark sales?" Naive implementations of data-mining queries will result in execution of laige numbers of decision-support queries, and may therefore take so much time to complete that the naive approach is completely infeasible. However, customer databases today can record much more about the customer or obtain the information from legitimate sources that are then integrated with

40 634 CHAPTER 11. INFORMATION INTEGRATION best attribute and threshold for those customers who have A < v, and we do

41 11.5. DATA MINING 635 For instance, documents that talk about databases might have occurrences of

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43 11.5. DATA MINING 637 Other Forms of Association Rules

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45 11.6. SUMMARY OF CHAPTER Summary of Chapter Integration of Information: Frequently, there exist a variety of databases

46 640 CHAPTER 11. INFORMATION INTEGRATION the fact table is the center of the star, and the dimension tables are the points. + The Cube Operator: When the MOLAP approach is chosen, a specific

47 11.7. REFERENCES FOR CHAPTER