1. Fundamentals, Design, and Implementation, 9/e COS 346 Day 25

2. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/2 Copyright © 2004 Agenda  Assignment 9 Corrected –4 A’s, 1 D and one non-submit  Assignment 10 Due  Assignment 11 Posted –Due May 6 10 AM  Quiz 4 –May 6 10 AM –Chap 9, 10, 12, 13 & 15  Capstones Presentation May 6 10AM –All deliverables Due  Today –Discussion on Sharing Enterprise Data

3. Fundamentals, Design, and Implementation, 9/e Chapter 15 Sharing Enterprise Data

4. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/4 Copyright © 2004 Database Processing Architectures  System architectures for enterprise database processing: –Teleprocessing system –Client-server system –File-sharing system –Distributed system

5. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/5 Copyright © 2004 Teleprocessing Systems  Classic architecture for multi-user database processing  Users operate dumb terminals or PC that emulate dumb terminals –User interface is usually simple and primitive  A single centralized computer processes communications control program, application programs, DBMS, and operating system

6. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/6 Copyright © 2004 Teleprocessing Systems

7. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/7 Copyright © 2004 Client-Server Systems  A client-server system consists of a network of computers connected via a LAN  Clients are personal computers used to process application programs  Servers are PCs or mainframes that stores DBMS and the data-management portion of the operating system

8. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/8 Copyright © 2004 Client-Server Systems

9. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/9 Copyright © 2004 File-Sharing Systems  This architecture was developed before the client- server architecture  File server and user computers are connected through LAN –File server provides access to files and other resources –User computers must contain a copy of DBMS and application programs  DBMS on user’s computer sends requests to the data management portion of the operating system on the file server for file-level processing –This cause more traffic across LAN than the client-server system

10. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/10 Copyright © 2004 File-Sharing Systems

11. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/11 Copyright © 2004 Distributed Database Systems  Distributed database systems use multiple computers to process the same database  Distributed processing: use multiple computers for applications or DBMS processing –Database is centralized –E.g., file-sharing, client-server, and distributed database system  Distributed database processing: distribute database to multiple computers –E.g., distributed database system

12. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/12 Copyright © 2004 Distributed Database Systems

13. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/13 Copyright © 2004 Database Partitioning  A vertical partition, or vertical fragment, refers to a table that is broken into two or more sets of columns  A horizontal partition, or horizontal fragment, refers to a table that is broken into two or more sets of rows  Mixed partition refers to a database broken into both horizontal and vertical partitions

14. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/14 Copyright © 2004 Types of Distributed Databases  Types of distributed database: –Nonpartitioned, nonreplicate –Partitioned, nonreplicated –Nonpartitioned, replicated –Partitioned, replicated  The greater the degree of partitioning and replication –The greater the flexibility, independence, and reliability –The greater the expense, control difficulty, and security problems

15. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/15 Copyright © 2004 Types of Distributed Databases

16. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/16 Copyright © 2004 Comparing DB Distribution Alternatives

17. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/17 Copyright © 2004 Distributed Processing Techniques  Three types of distributed database processing  Downloading of read-only data: only one computer updates data, but multiple computers are sent copies to process  Updating by a designated computer: allows data update requests to originate on multiple computers, but to transmit those update requests to a designated computer for processing –Database at the designated computers must be periodically synchronized

18. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/18 Copyright © 2004 Distributed Processing Techniques (cont.)  Updating by multiple computers: allows multiple updates on the same data at multiple sites –Three types of distributed update conflict can occur: Loss of uniqueness Lost updates due to concurrent transactions Updates of deleted data  Coordinating distributed atomic transactions is difficult and requires a two-phase commit  The OLE Distributed Transaction Server and Java Enterprise Beans are two technologies for dealing with these problems

19. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/19 Copyright © 2004 Downloading Data  Powerful personal computers enable user to download enterprise data for local processing  Users can query and report on downloaded data using DBMS products on their machines  Normally, users are not allowed to update and return data to prevent data integrity problems  A Web server can be used to publish downloaded data

20. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/20 Copyright © 2004 Potential Problems of Downloaded Databases  Coordination –Downloaded data must conform to database constraints –Local updates must be coordinated with downloads  Consistency –In general, downloaded data should not be updated –Applications need features to prevent updating –Users should be made aware of possible problems

21. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/21 Copyright © 2004 Potential Problems of Downloaded Databases (cont.)  Access Control –Data may be replicated on many computers –Procedures to control data access are more complicated  Potential for Computer Crime –Illegal copying is difficult to prevent –Diskettes and illegal online access are easy to conceal –Risk may prevent the development of downloaded data applications

22. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/22 Copyright © 2004 Processing Downloaded Data with a Web Server

23. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/23 Copyright © 2004 OLAP  On Line Analytical Processing (OLAP) is a new way of presenting information  With it, data is viewed in cubes that have axes, dimensions, measures, slices, and levels  Cube refers to –Underlying semantic structure that is used to interpret data –A particular materialization of data in such a semantic structure

24. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/24 Copyright © 2004 Example: Relational Source Data

25. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/25 Copyright © 2004 Example: OLAP Cube

26. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/26 Copyright © 2004 OLAP Terminology  OLAP hypercube: means a data display with an unlimited number of axes (Dimensions)

27. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/27 Copyright © 2004 Example: Cube Data Definition CREATE CUBE HousingSalesCube ( DIMENSION Time TYPE TIME, LEVEL Year TYPE YEAR, LEVEL Quarter TYPE QUARTER, LEVEL Month TYPE MONTH, DIMENSION Location, LEVEL USA TYPE ALL, LEVEL State, LEVEL City, DIMENSION HousingCategory, DIMENSION HousingType, MEASURE SalesPrice, FUNCTION AVG MEASURE AskingPrice, FUNCTION AVG )

28. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/28 Copyright © 2004 Example: Multidimensional SELECT Statement SELECT CROSSJOIN ({Existing Structure, New Construction}, {California.Children, Nevada}) ON COLUMNS, {1998.Q1.Children, 1998.Q2, 1998.Q3, 1998.Q4, 1999.Q1.Children, 1999.Q2, 1999.Q3, 1999.Q4} ON ROWS FROM HousingSalesCube WHERE (SalesPrice, HousingType = ‘SingleFamily’)

29. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/29 Copyright © 2004 OLAP Schema Structures  Star schema: every dimension table is adjacent to the table storing the measure values –These tables may or may not be normalized  Snowflake schema: there can be multilevel, normalized tables  In general, the star schema requires greater storage, but it is faster to process than the snowflake schema

30. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/30 Copyright © 2004 Example: Star Schema

31. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/31 Copyright © 2004 Example: Snowflake Schema

32. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/32 Copyright © 2004 OLAP Storage Alternatives  Three different means for storing OLAP data  ROLAP (relational OLAP): relational DBMS with extensions is sufficient to meet OLAP requirements  MOLAP (multidimensional OLAP): a specialized multidimensional processor is necessary to produce acceptable OLAP performance  HOLAP (hybrid OLAP): both DBMS products and specialized OLAP engines have a role and can be used to advantage

33. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/33 Copyright © 2004 Microsoft OLAP Architecture  Microsoft has extended OLE DB and ADO for OLAP  OLE DB for OLAP includes –A dataset object –ADO MD: new objects for processing dataset objects in ways similar to recordset objects –Pivot Table Service: an OLAP processor available as Office 2000/XP and Windows 2000/XP service  Microsoft’s architecture moves much OLAP processing to client computers  Whether this will be acceptable for the processing of data on enterprise servers is as yet unknown

34. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/34 Copyright © 2004 Microsoft OLAP Architecture

35. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/35 Copyright © 2004 Client Side Pivot Tables

36. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/36 Copyright © 2004 Data Warehouse  A data warehouse is a store of enterprise data that is designed to facilitate management decision-making  Goal: to increase the value of the organization’s data asset  Role: to store extracts from operational data and make those extracts available to users in a useful format –Data can be extracts from databases, files, images, recordings, photos, external data, etc.

37. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/37 Copyright © 2004 Data Warehouse

38. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/38 Copyright © 2004 Data Warehouse Components  Data extraction tools  Extracted data  Metadata of warehouse contents  Warehouse DBMS(s) and OLAP servers  Warehouse data management tools  Data delivery programs  End-user analysis tools  User training courses and materials  Warehouse consultants

39. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/39 Copyright © 2004 Data Warehouse Requirements  Queries and reports with variable structure  User-specified data aggregation  User-specified drill down  Graphical outputs  Integration with domain-specific programs

40. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/40 Copyright © 2004 Challenges for Data Warehouses  Inconsistent data –When data are integrated, inconsistencies can develop due to timing and domain differences –Solution: create metadata to describe both timing and domains of source data  Tool Integration –Because of the many tools required in a data warehouse, tools will have different user interfaces and inconsistent means of importing and exporting data, and it may be difficult to obtain technical support

41. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/41 Copyright © 2004 Challenges for Data Warehouses  Lack of tools for managing the data warehouse –The organization may have to develop its own tools for managing non-relational data and for maintaining appropriate metadata. Such development is difficult and expensive  Ad hoc nature of requirements –Such requests are difficult to satisfy –Solution: create datamart, i.e,, limited-scope data warehouses

42. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/42 Copyright © 2004 Data Marts  A data mart is a limited-scope data warehouse  A data mart is easier to manage than the enterprise data warehouse because –It has a much smaller domain –It can be restricted To a particular type of input data To a particular business function To a particular business unit or geographic area

43. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/43 Copyright © 2004 Enterprise Data Sharing Continuum

44. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/44 Copyright © 2004 Data Administration  Data are an important organizational asset that can support both operations and management decisionmaking  The purpose of offices of data administration is to guard and protect the data and to ensure that they are used effectively

45. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/45 Copyright © 2004 Data Administration Challenges  Many types of data exist  Basic categories of data are not obvious  The same data can have many names  The same data can have many descriptions and formats  Data are changed often concurrently  Political-organizational issues complicate operational issues

46. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/46 Copyright © 2004 Functions of Data Administration  Marketing –Communicate existence of data administration to organization –Explain reason for existence of standards, policies, and guidelines –Describe in a positive light the services provided  Data Standards –Establish standard means for describing data items. Standards include name, definition, description, processing restrictions, etc. –Establish data proponents

47. Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeChapter 15/47 Copyright © 2004 Functions of Data Administration  Data Policies –Establish organization-wide data policy, e.g., security, data proponency, and distribution  Forum for Data Conflict Resolution –Establish procedures for reporting conflicts –Provide means for hearing all perspectives and views –Have authority to make decision to resolve conflict  Return on Organization's Data Investment –Focus attention on value of data investment –Investigate new methodologies and technologies –Take proactive attitude toward information management

48. Fundamentals, Design, and Implementation, 9/e Chapter 15 Sharing Enterprise Data