1. What is Database? Database란 무엇인가
Database Overview
What is Database?
Database란 무엇인가

2. Database Intro: Why & How
Data vs. Information
Data is a collection of facts.
Information is data processed for knowledge.
Changing data into information
Organize data so that it can be viewed in a useful form.
What form will the derived information take?
How will information be extracted?
What data to collect, how & why?
Requirements
Identify the Context of data → Metadata
Organize 정리, 체계화, 조직화 → Structured Data 구조적 데이터
Summarize 요약 → Information
Database Design

3. Data → Information: 1.Identify Context
Obama, Barack H
Bush, George H W
Bush, George W
Clinton, William J
Carter, James E
Context
Living presidents, United States, 2016/1/1
Name (last name, first name middle initial), birthdate (YYYYMMDD)
Class Roster, Database Design Course, LIS Department, KNU, Spring 2016
Name (last name, first name middle initial), student ID
Database Design

4. Data → Information: 2.Organize Data
Identify metadata (metadata의 식별)
Identify additional data items ( data 식별을 위한 부가적 요소)
Course Title
Database System
Course ID
gDB-s16
Credit Hours
3.0
Class Time
Monday 7-9:30 p.m.
Semester
Spring 2017
Instructor
Yang, Kiduk
Department
Library & Information Science
College
School of Social Science
University
Kyungpook National University
Lname
Fname
Init
Stud_ID
Obama
Barack H
Bush
George HW W
Clinton
William J
Carter
James E
Major
Level
GPA
LIS
MS1
3.8
TCOM
2.1
ACCT
MS2
3.0 CS
PHD1
3.9
3.7
Database Design

5. Data → Information: 3.Summarize
Patterns, Trends & Visualization
Enrollment Pie Chart
Enrollment over Time
10% DS 30
Enrollment
15%
ACCT 20
45%
LIS
15% CS 15 10
15%
TCOM 5
ACCT = Accounting
CS = Computer Science
DS = Data Science
LIS = Library & Information Science
TCOM = Telecommunication
0s 0f 1s 1f 2s 2f 3s 3f 4s 4f 5s 5f 6s
Semester
Database Design

6. Database Intro: What Function Characteristics
Store 저장 / Retrieve 검색 / View 검토 data efficiently & effectively.
Characteristics
A collection of organized data related to a particular subject/purpose
Structured data 구조적 데이터, Security 보안, Control 통제
DataBase Management System (DBMS)
(Data) Storage 저장, Processing 처리/가공, Retrieval 검색
User Interface
Data Entry 데이터 입력, Search 입력, View/Report 검토/보고
Database Design

7. Database: Definitions
Collection of related data 관련된 데이터 and its metadata organized in a structured format 구조적 형식
for optimized information management 정보 관리
Database Management System (DBMS) 
Software that enables easy creation 구축, modification 변경, & access 접속 of databases
for efficient and effective database management 데이터베이스 관리
Database System 
Integrated system 통합 시스템 of hardware, software, data, procedures, & people
that define 결정 and regulate 규제 the collection, storage, management, & use of data within a database environment 데이터베이스 환경
End user data – raw facts of interests to the end user
Metadata – description of data characteristics and relationships
Organization of data helps:
improve data accuracy
improve timely access
correlation and comparison
DBMS: a collection of programs that manages the database structure, control access to data
- Makes data management more efficient and effective
- Query language allows quick answers to ad hoc queries
- Provides better access to more and better-managed data
- Promotes integrated view of organization’s operations
- Reduces the probability of inconsistent data
Database Design

8. Database Management System
Software that enables easy creation 구축, modification 변경, & access 접속 of databases for efficient and effective database management 데이터베이스 관리
→ Manages interaction between end users and database 이용자와 DB사이의 상호 작용 관리
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database Design

9. Database System Environment
Hardware
Software OS DBMS Applications
People
Procedures
Data
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database System

10. Evolution of Database System
Database Overview
Evolution of Database System

11. Evolution of Database Database 발전 1960s 1970s 1980s 1990s 2000+
File-based
Hierarchical
Network
Relational
Object-oriented
Entity-Relationship
Web-based
NoSQL
NewSQL
Database Design

12. Database: Historical Roots (기원)
Manual File System
To keep track of data
Used tagged file folders in a filing cabinet
Organized according to expected use
e.g. file per customer
Easy to create, but hard to
locate data
aggregate/summarize data
Computerized File System
To accommodate the data growth and information need
Manual file system structures were duplicated in the computer
Data Processing (DP) specialists wrote customized programs to
write, delete, update data (i.e. management)
extract and present data in various formats (i.e. report)
Database Design

13. Database Systems: Design, Implementation, & Management: Rob & Coronel
File System: Example
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database Design

14. File System: Weakness Weakness Problems Implications
“Islands of data” in scattered file systems 분산된 파일시스템.
Problems
Duplication 중복
Same data may be stored in multiple files
Inconsistency 불일치
Same data may be stored with different values/formats
Rigidity 경직성
Requires customized programming to implement any changes
Cannot do ad-hoc queries 즉석질의 불가
Implications
Waste of space
Data inaccuracies 오류
High overhead 간접비용 of data manipulation and maintenance
Database Design

15. File System: Problem Case
CUSTOMER file
AGENT file
SALES file
A_Name (15 char)
A_Name (20 char)
AGENT (20 char)
Carol Johnson
Carol T. Johnson
Carol J. Smith
Inconsistent field name, field size
inconsistent data values
data duplication
Database Design

16. Database System vs. File System
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database Design

17. Hierarchical Data Model 계층적 데이터 모델
Hierarchical Model
To manage large amount of data for complex manufacturing projects
Information Management System developed by Rockwell & IBM
Files connected in Parent-Child (1:M) relationships
1 Parent - Multiple Children
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database Design

18. Hierarchical Data Model 계층적 데이터 모델
Strengths
Conceptual Simplicity 개념적 단순성
Groups of data could be related to each other
Related data could be viewed together
Centralization of data
Reduced redundancy  중복 and promoted consistency  일관성
Weaknesses
Limited representation of data relationships
Did not allow Many-to-Many (M:N) relations
Structural Dependence 구조 의존
Data access requires physical storage path
Complex Implementation 복잡한 구현
Required in-depth knowledge of physical data storage
Lack of Standards 표준 부족
Limited portability
Database Design

19. Network Data Model 네트워크 데이터 모델
Network Model
Extension of Hierarchical Model 계층모델의 확장형
Composed of Owner-Member (Parent-Child) sets
To represent Many-to-Many (M:N) relationships
Multiple Parents – Multiple Children
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database Design

20. Relational Data Model 관계형 데이터 모델
Problems with legacy database systems
Required excessive effort to maintain
Data manipulation (programs) too dependent on physical file structure
Hard to manipulate by end-users
No capacity for ad-hoc query (must rely on DB programmers).
Relational Model
E. F. Codd’s proposal
Separated the notion of physical representation (machine-view) from logical representation (human-view)
Eliminated pointers and used tables to represent data
Considered ingenious but computationally impractical in 1970
Dominant database model of today
Separation of design from implementation → Flexible
Ad-hoc queries → Structured Query Language (SQL)
Database Design

21. Relational Database: Example
Tables (i.e. Relations)
Provide a logical “human-level” view of the data and associations among groups of data
Organize data into rows 행 (records/tuples) and columns 열 (attributes)
Are related via shared attribute(s)
Database Design

22. Entity Relationship Model
Peter Chen’s Landmark Paper (1976)
“The Relationship Model: Toward a Unified View of Data”
Graphical representation of entities and their relationships
Based on Entity, Attributes & Relationships
Entity → e.g. EMPLOYEE
Thing about which data are to be collected and stored
Attributes → e.g. SSN, last name, first name
Characteristics of the entity
Relationships → i.e. 1:M, M:N, 1:1
Associations between entities
Complements the relational data model concepts
Helps to visualize structure and content of data groups
Entity Relationship Diagram (ERD) → Tool for conceptual data modeling → Formalizes a way to describe relationships between groups of data
Database Design

23. E-R Diagram: Chen Model
Entity 개체
represented by a rectangle with its name in capital letters.
Relationship 관계
represented by an active or passive verb inside the diamond that connects the related entities.
Connectivity 관계유형
i.e., types of relationship
written next to each entity box.
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database Design

24. E-R Diagram: Crow’s Foot Model
Entity 개체
represented by a rectangle with its name in capital letters.
Relationship 관계
represented by an active or passive verb that connects the related entities.
Connectivity 관계유형
indicated by symbols next to entities.
2 vertical lines for 1
“crow’s foot” for M
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database Design

25. E-R Model: Pros & Cons Advantages Disadvantages
Exceptional conceptual simplicity
Easily viewed and understood representation of database
Facilitates database design and management
Integration with the relational database model
Enables better database design via conceptual modeling
Disadvantages
Incomplete model on its own
Limited representational power
cannot model data constraints not tied to entity relationships
e.g. attribute constraints
cannot represent relationships between attributes within entities
No data manipulation language (e.g. SQL)
Loss of information content
Hard to include attributes in ERD
Database Design

26. Object-Oriented Database 객체지향
Semantic Data Model (SDM)
Modeled both data and their relationships in a single structure (object)
Developed by Hammer & McLeod in 1981
Object-oriented concepts became popular in 1990s
Modularity facilitated program reuse and construction of complex structures
Ability to handle complex data types (e.g. multimedia data)
Object-Oriented Database Model (OODBM)
Maintains the advantages of the ER model but adds more features
Object = entity + relationships (between & within entity)
consists of attributes & methods
methods are all relevant operations that can be performed on an object
Class  Template for objects
e.g. EMPLOYEE class = (employ1 object, employ2 object, …)
organized in a class hierarchy
e.g. PERSON > EMPLOYEE, CUSTOMER
Incorporates the notion of inheritance
attributes and methods of a class are inherited by its descendent classes
Database Design

27. OO Database Model vs. E-R Model
OODBM: - can accommodate relationships within a object - objects to be used as building blocks for autonomous structures
Database Systems: Design, Implementation, & Management: Rob & Coronel
Database Design

28. Object-Oriented Database: Pros & Cons
Advantages
Semantic representation of data
Fuller and more meaningful description of data via object
Modularity, reusability, inheritance
Ability to handle
Complex data
Sophisticated information requirements
Disadvantages
Lack of standards
No standard data access method
Complex navigational data access
Class hierarchy traversal
Steep learning curve
Difficult to design and implement properly
High system overhead
Slow transactions
Database Design

29. Web Database Not a database model, but a system
For storing information that can be accessed via Web
That supports complex data types & relationships
In a Client-Server architecture
Server hosts database & DBMS (e.g., MySQL)
Client accesses the server for database use
Client
Initiates a Connection
Server
Waits & Responds to Incoming Connections
Database
Web Client
(e.g. Chrome)
HTTP request
Web Server
(e.g. Apache)
Data request
DB Server
(e.g. MySQL)
Webpage
Retrieved data
Database Design

30. NoSQL/NewSQL Database
NoSQL (Not Only SQL)
Non-relational: e.g., objects instead tables
For big (unstructured, distributed) data & real-time Web applications
More scalable & better performance
Flexible & agile development
NewSQL
NoSQL + Relational
Consistent
Scalable
Flexible
Database Design