CHAPTER 8 DECISION SUPPORT V2 ADVANCED DATABASE SYSTEMS. Assist. Prof. Dr. Volkan TUNALI

Transcription

1 CHAPTER 8 DECISION SUPPORT V2 ADVANCED DATABASE SYSTEMS Assist. Prof. Dr. Volkan TUNALI

2 Topics 2 Business Intelligence (BI) Decision Support System (DSS) Data Warehouse Online Analytical Processing (OLAP) Multidimensional Analysis Star Schema Data Mining SQL Extensions for OLAP Cross-Tabulations and Excel Pivot Tables

3 Business Intelligence (BI) 3 A comprehensive, cohesive, and integrated set of tools and processes, Used to capture, collect, integrate, store, and analyze data, With the purpose of generating and presenting information used to support business decision making. BI is about creating intelligence about a business.

4 Business Intelligence (BI) 4 BI is a framework that allows a business to transform data into information, information into knowledge, and knowledge into wisdom.

5 Business Intelligence (BI) 5 BI involves the following general steps: Collecting and storing operational data. Aggregating the operational data into decision support data. Analyzing decision support data to generate information. Presenting such information to the end user to support business decisions. Making business decisions, which in turn generate more data that is collected, stored, etc. (restarting the process). Monitoring results to evaluate outcomes of the business decisions (providing more data to be collected, stored, etc.).

6 Business Intelligence Framework 6

7 Business Intelligence Framework 7 The focus of traditional information systems is on operational automation and reporting. In contrast, BI tools focus on the strategic and tactical use of information.

8 BI Architectural Components 8

9 BI Architectural Components 9 Data extraction, transformation, and loading (ETL) tools collect, filter, integrate, and aggregate operational data to be saved into a data store optimized for decision support. The data store is optimized for decision support and is generally represented by a data warehouse or a data mart.

10 ETL Process 10

11 Decision Support System (DSS) 11 A decision support system (DSS) is an arrangement of computerized tools used to assist managerial decision making within a business. Decision support systems were the precursors of modern BI systems. A DSS typically has a much narrower focus and reach than a BI solution.

12 Operational Data vs. Decision Support Data 12 From the data analyst s point of view, decision support data differ from operational data in three main areas: time span, granularity, and dimensionality.

13 Transforming Operational Data 13

14 Operational vs. Decision Support Data 14

15 Data Warehouse 15 Data Warehouse is a collection of data that provides support for decision making, with the following characteristics: integrated, subject-oriented, time-variant, nonvolatile. Data warehouse is usually a read-only database optimized for data analysis and query processing. A Data Mart is a small, single-subject data warehouse subset that provides decision support to a small group of people.

16 Online Analytical Processing (OLAP) 16 OLAP tools create an advanced data analysis environment that supports decision making, business modeling, and operations research. OLAP systems share four main characteristics: Multidimensional data analysis techniques. Advanced database support. Easy-to-use end-user interfaces. Client/server architecture.

17 Multidimensional Analysis 17

18 Multidimensional Analysis 18

19 OLAP Architecture 19

20 OLAP Architecture 20

21 Multidimensional (MOLAP) 21 End users visualize the stored data as a 3D cube known as a data cube. The data cubes can grow to n number of dimensions, thus becoming hypercubes.

22 MOLAP Client/Server Architecture 22

23 Star Schema 23 Star Schema is a data-modeling technique used to map multidimensional decision support data into a relational database. In effect, the star schema creates the near equivalent of a multidimensional database schema from the existing relational database.

24 Star Schema Components 24 Basic star schema has four components: facts, dimensions, attributes, and attribute hierarchies.

25 Facts 25 Facts are numeric measurements (values) that represent a specific business aspect or activity. e.g. Sales units, costs, prices, and revenues Fact table contains facts that are linked through their dimensions. Computed or derived facts are sometimes called metrics to differentiate them from stored facts. The fact table is updated periodically (daily, weekly, monthly, and so on) with data from operational databases.

26 Dimensions 26 Dimensions are qualifying characteristics that provide additional perspectives to a given fact. e.g. Sales might be compared by product from region to region and from one time period to the next. Dimensions are normally stored in dimension tables.

27 Simple Star Schema 27

28 Attributes 28 Dimensions provide descriptive characteristics about the facts through their attributes.

29 3D View of Sales 29

30 Slice-and-Dice View of Sales 30

31 Attribute Hierarchies 31 Attributes within dimensions can be ordered in a well-defined attribute hierarchy. The attribute hierarchy provides a top-down data organization that is used for two main purposes: aggregation drill-down/roll-up data analysis

32 Location Attribute Hierarchy 32

33 Attribute Hierarchies 33

34 Star Schema Representation 34 Facts and dimensions are normally represented by physical tables in the data warehouse database. The fact table is related to each dimension table in a many-to-one (M:1) relationship. The fact table is related to many dimension tables.

35 Star Schema for Sales 35

36 Star Schema for Orders 36

37 Snowflake Schema 37

38 Data Mining 38 A typical data analysis tool relies on the end users to define the problem, select the data, and initiate the appropriate data analyses to generate the information that helps model and solve problems that the end users uncover. In contrast to the traditional (reactive) BI tools, data mining is proactive. Data-mining tools automatically search the data for anomalies and possible relationships, thereby identifying problems that have not yet been identified by the end user.

39 Extracting Knowledge from Data 39

40 Data Mining Phases 40

41 SQL Extensions for OLAP 41 2 useful extensions to the GROUP BY: ROLLUP CUBE

42 The ROLLUP Extension 42 SELECT column1, column2 [,...], aggregate_function(expression) FROM table1 [,table2, ] [WHERE condition] GROUP BY ROLLUP (column1, column2 [,...]) [HAVING condition] [ORDER BY column1 [, column2, ]]

43 The ROLLUP Extension 43

44 The CUBE Extension 44 SELECT column1 [, column2,...], aggregate_function(expression) FROM table1 [,table2, ] [WHERE condition] GROUP BY CUBE (column1, column2 [, ]) [HAVING condition] [ORDER BY column1 [, column2, ]]

45 The CUBE Extension 45

46 Cross-Tabulations and Excel Pivot Tables 46

47 Cross-Tabulations and Excel Pivot Tables 47