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1 DBMS : Past, Present, and the Future hjk@oopsla.snu.ac.kr

2 1 Table of Contents

3 2 DBMS?

scheme data data manipulation language pre-compiler query processor query data data definition

4 3 DBMS Architecture naive users naive users programmers application casual users casual users administrator database application programs system system calls calls query query database scheme data data manipulation language pre-compiler query processor query data data definition language compiler application programs object object database manager DBMS manager file file Disk storage

5 4 Table of Contents

6 5 DBMS

7 6 Network DB Example Lowery Maple Queens Hodges SideHill Brooklyn Shiver North Bronx ?

8 7 sum:=0 Network DB query example get first customer where customer.name= Shiver and customer.city = Bronx ; while DB_status = 0 do begin sum:=sum+customer.amount; get next customer where customer.name = Shiver and customer.city = Bronx ; end print(sum);

9 8

10 9 DBMS name street city amount Lowerly Maple Queens 900 Shiver North Bronx 556 Shiver North Bronx 647 Hodges SideHill Brooklyn 801 Hodges SideHill Brooklyn 647 Select sum(amount) from customer where customer.name = Shiver and customer.city= Bronx ;

11 10 RDBMS

12 11 RDBMS R&D

13 12 DB

14 13 (OO) DBMS

15 14 OODBMS?

16 15 interface Customer { attribute string name; DBMS IDL relationship Set<Deposit> deposit inverse Deposit::owned_by; } interface Branch { attribute string street; attribute string city; relationship Set<Deposit> belong inverse Deposit::branch; } interface Deposit { relationship Customer owned_by inverse Customer::branch; relationship Branch branch inverse Branch::belong; float balance; }

17 16 OO DBMS OQL query select sum(customer.deposit.balance) from Customer customer where customer.name = Shiver and customer.deposit.branch.city = Bronx ;

18 17 OODBMS

19 18 (OR) DBMS

20 19 ORDBMS name Lowerly Shiver Shiver Hodges Hodges Branch(street, city) {Maple, Queens} {North, Bronx} {North, Bronx} {SideHill, Brooklyn} {SideHill, Brooklyn} amount

21 20 ORDBMS

22 21 ORDBMS

23 22 ORDBMS Feature Informix IBM DB2 Oracle8 UDT O O O Strong typing O O O Data replication X X O UDF O O O Func. overloading O O O Func. resolution O O O

24 23 ORDBMS Feature Informix IBM DB2 Oracle8 LOB O O O External data O O O Integrated searchable content O O O 3GL/4GL O/X O/O O/O OO language O O X Predefined extensions O O O

25 24 ORDBMS Feature Informix IBM DB2 Oracle8 Platforms Unix from DG, DEC, HP, IMB, NCR, Sequent, SGI, and Sun Solaris; Windows NT Unix from HP, IBM, and Sun Solaris; Windows NT; OS/2; Unix from DEC, IBM, HP, Sequent, and Sun Solaris; Windows NT

26 25 Table of Contents

27 Database Market Share 42.4% Source: IDC, June % 7.8% 5.9% 3.9% Oracle IBM Microsoft Informix Sybase

27 1999 Database Market -Worldwide(includes Non-RDBMS) 3.0% 4.3% 3.3% 16.

28 Database Market -Worldwide(includes Non-RDBMS) 3.0% 4.3% 3.3% 16.0% 31.1% 13.1% 29.9% Source : Dataquest DBMS Market Share Numbers, May 2000

28 1999 UNIX RDBMS Market 3% 6% 10.0% 6% 12% 63.

29 UNIX RDBMS Market 3% 6% 10.0% 6% 12% 63.0% Source : Dataquest DBMS Market Share Numbers, May 2000

29 1999 NT RDBMS Market 0.7% 3.0% 15.0% 40.0% 35.

30 NT RDBMS Market 0.7% 3.0% 15.0% 40.0% 35.0% Source : Dataquest DBMS Market Share Numbers, May 2000

31 30 Example : Oracle8i Transaction Processing Secure Parallel Internet Extensible Distributed Warehousing Decision Support Object Component

32 31 Example: Oracle8i Oracle8i Oracle8i Oracle8i Oracle8i Oracle8i

33 32 Table of Contents

34 33 Current Database Issues

35 34 Asilomar Report: DBMS research trend(1)

36 35 Asilomar Report: DBMS research trend(2)

37 36 The Grand Challenge!!!

38 37 Bio Technology Data

39 38 XML?

40 39 HTML & XML <tr> <td> <font color= red > </font> </td> <td> </td> </tr> <tr> <td> <b> </b> </td> <person> <name> </name> <city> </city> </person>

41 40 Basic Representation Bib <Bib> </Bib> <paper id= o2 references= o3 > <author>abiteboul </author> </paper> <book id= o3 > <author> Hull </author> <title> Foundations of Data Bases </title> <publisher> Addison Wesley </publisher> </book> author Abiteboul paper book reference 2 3 author Hull 1 title publisher Foundations Addison Of DataBasesWesley XML data OEM Model

42 41 Why XML?

43 What are XML for? 42 XML XML?

44 43 Database Data Warehouse Knowledge Discovery Processing: Data mining useful, interesting hidden information

45 44 Data Warehouse(1)

46 45 Data Warehouse(2) Sales Volumes Jan time Feb Product Mar Wong Dewitt A B Stonebreaker Sales person C

47 46 Data Mining(1)?

48 47 Data Mining(2)

49 48 Security and Directory(1) Privacy of Communications Sensitive Data Storage Granular Access Control Is an order read or modified in transit? Network encryption Is your credit card # stored in clear? Encryption of stored data Can a customer see only her own order? Virtual Private Database

50 49 Security and Directory(2) Know your Users Scalability Ease of Use Who is accessing the data from the web? Strong authentication Can you support 100,000s of users? Directory integration Is it easy to use for users & administrators? Schema-independent users

51 50 High Availability(1) Computer A Computer B Node A in a cluster fails, users are migrated Computer A Computer B

52 51 High Availability(2) Orders Orders Orders Queries/Inserts/Updates/Deletes Parallel Recovery from Failure Partition Unavailable

53 52 Manageability OLTP User DSS User Batch Process ing Database Resource Manager OLTP updates and queries: high priority DSS queries: medium priority Batch: low priority

54 53 Table of Contents

55 54 Key Focus Areas for Oracle9i Availability Scalability & Performance Security Development Platform Manageability Windows 2000 Integration Internet Content Management B2C and B2B ebusiness Packaged Applications Business Intelligence

56 55 Oracle 9i Breakthrough Features Oracle9i Real Application Clusters transparent scalability Oracle9i Data Guard zero data loss disaster protection Self-tuning, Self-managing Database increase DBA productivity Built-in in OLAP, Data-mining, ETL Services Business Intelligence on an Internet Scale Real-Time Personalization The only real-time recommendation engine

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