Directory UMM :Networking Manual:computer_network_books:
DATABASE MANAGEMENT
SYSTEMS (DBMS)
by
Prof. Kudang B. Seminar, MSc, PhD
e-mail: kseminar@ipb.ac.id
Database sebagai Komponen Vital Sistem
Informasi
E
DA
R
A
TA
W
WA
N
I
A
RE
Performance
R
B
Control System
S
O
Data
F
T
W
A
R
E
Process
Data Store
N E TWAR E
H
A
R
Info
D
W
A
R
E
Data vs Information
Data: raw facts or observations
Information : data that have
been transformed into a
meaningful and useful context
for specific end users
Data
Information
Data Sales person
Sales Values
Sales Units
Data
Processing
Sales Analysis
Sample Business Application
Sample Tabular View of Sales
Sample Pivot Chart for Sale Analysis
Akusisi Data Geografis
Data Geografis Yang Tersimpan
Produk Informasi Geografis
Basis Data (Database)
Koleksi terpadu dari data-data yang saling
berkaitan yang dirancang untuk suatu
enterprise.
Data
Dosen
Data
Mhs
Data
Alumni
Data
Mkul
Analisis Kebutuhan Data
(Data Requirement Analyisis)
• Think and conceptualize business objects and logic
• Identify information needed -> then what data are needed
• Formulate what computer applications are needed?
Dokumentasikan hasil Analisis dengan Alat Bantu
Permodelan (Modeling Tools)
Kasus Contoh: Data Requirement Analysis
Forward Support Analysis
Sources of
Data
Supporting
Data
Supporting
Information
Management
Objectives
Management
Functions
Backward Requirement Analysis
• BAAK
• KRS
• Academic Progress
• Monitoring Student Progress …
• Monitoring
• Faculty
• Transkrip
• Treated Students
• Directing Student Research …
• Directing
• Dept.
• Supervisi
• Student Potentials
• Planning for Remedial Efforts .
• Planning
• Study
Program
• Research
List
• Academic Problem
• Acting on Remedial Plan …
• Acting
Contoh Kasus: Analisis Kebutuhan Data Mhs
Data
Info
KRS, Transkrip
IPK Kumulatif
Status Akademik
Mhs
Warning 1, 2, 3,
rekomendasi
D.O or Extended
Minat riset &
PTA mhs, Data
PTA
Profile minat
riset & PTA
mhs, Beban
PTA
Analisis minat riset
& PTA mhs
Alokasi PTA utk
mhs
Alokasi final PTA
utk mhs
Catatan riset
mhs, Trankrip,
KRS.
Kemajuan riset
mhs
Status Akademik
Mhs
Rekomendasi
perlakuan
Eksekusi
perlakuan
Catatan riset
mhs, Trankrip,
KRS
Profile
kelulusan mhs:
lama studi &
prestasi akad.
Analisis kelulusan:
rerata lama studi,
ranking akademik
Rekomendasi
program
akselerasi studi
Eksekusi
akselerasi studi
Data= Info=
Data1..n
Info1..n
Monitoring
Directing
Acting
Management Functions = Monitoring
Directing Acting Mencapai
Target Academic Excellence?
Utilisasi Vs Ketersedian Informasi
•
•
•
•
Ada dan Diperlukan
Tak ada dan Diperlukan
Ada dan Tak Diperlukan
Tak Ada dan Tak Diperlukan
Perlu
Ada
Tak Ada
Tak Perlu
Data Acquisition &
Information Production
Database Management Systems (DBMS)
Koleksi terpadu dari sekumpulan program (utilitas) yang
digunakan untuk mengakses dan merawat database
Users
DBMS
Database
Utilitas
Application Programs on Top of DBMS
Users
Application programs
DBMS
Database
Keuntungan DBMS
• Data menjadi shareable resources bagi berbagai
user dan aplikasi
• Metoda akses, penggunaan, dan perawatan
data menjadi seragam dan konsisten
• Pengulangan (redundancy) data dan
kemajemukan struktur data diminimisasikan
• Ketaktergantungan data terhadap program
aplikasi (data independence)
• Hubungan/relasi logik (logical relationship)
antar data terpelihara secara sistematik.
Conventional Data Management
Application
Application
• Data belongs to a certain application programs ; therefore it is
difficult to share data among application programs
• Data lifetime is limited (dependent ) to application program lifetime.
• Data redundancy and inconsistency will likely occur
• Non-uniform access method, data usage and maintenance.
• Incompatibility of data among application programs
Examples of software tools in DBMS
• Designing : ERD (Entity Relationship Diagram), DDL (Data
Definition Language)
• Inputing & Manipulating: DML (Data Modification
Language), QL (Query Language), Multimedia processor
• Searching & Retrieving: QL (Query Language): SQL * QBE
• Converting & Squeezing: Encoder & Decoder, Data
Converter & Squeezer, Multimedia processor
• Optimizing : Data Organizer & Analyzer
• Calculating: Math & statistical functions
• Presenting: Report Generator, Multimedia Processor
DBMS Approach Enables Resource Sharing Among
Applications and Users
Multiple Systems
Shareable
Resources
Data Management Life Cycle
• Need of changes
Real World
• Observing
• Identifying
• Updating
• Monitoring
• Protecting
• Browsing
• Conceptualizing
• Representing
• Structuring
• Analyzing
• Optimizing
• Coding
Data Modeling: Methods & Tools
Why Modeling?
Order
“Modeling captures essential
parts of the system.”
Item
Dr. James Rumbaugh
Ship via
Business Process
Visual Modeling is modeling
using standard graphical
notations: chart, diagrams,
objects, symbols
Copyright © 1997 by Rational Software Corporation
Data Model
Definition: Integrated collection of concepts,
theories, axioms, constraints for description,
organization, validation, and interpretation of data.
Usage: a fundamental set of tools & methods to
consistently & uniformly view, organize, and treat
database .
Types Data Models
Record-Based
Model
Relational
Hierarchical
Network
Object-Based
Model
Entity-relationship
Semantic
Functional
Object Oriented
Steps of Designing DBMS
• Determine what to store
• Determine what relations exists
• Determine what data services are needed
• Determine what data model is suitable
Data Warehouse
Kudang B. Seminar
What is Data warehouse?
• Data warehouse as a subject- oriented,
integrated, time variant, non-volatile
collection of data in support of
management’s decision making process
• Data warehouse systems consist of a set
of programs that extract data from the
operational environment, a database
that maintains data warehouse data,
and systems that provide data to users
The Goal of Data Ware
House?
•to provide a "single image
of business reality" for the
organization
Fundamental Ideas Behind the
Successful Data Warehousing
• Operational vs. Decision Support Applications : One impetus for
•
•
•
•
data warehouse is the unsuitability of traditional operational
applications for typical decision support usage patterns;
Primitive vs. Derived Data : A critical success factor in data
warehouse design is understanding knowledge workers’
demand demand for detailed vs. summary data;
Time Series Data: Data warehouse often supports analysis of
trends over time and comparisons of current vs. historical data;
Data Administration: Another critical success factor is senior
management commitment to maintenance of the quality of
corporate data
Systems Architecture: A system must be architected when it is
very complex, requires the integration of many disciplines, or is
developed in the face of uncertain requirements.
Alignment of data warehouse entities with the
business structure
Corporate Data for
Warehouses
A
corporate data warehouse is a
process by which related data
from many operational systems is
merged to provide a single,
integrated business information
view that spans all business
divisions.
SYSTEMS (DBMS)
by
Prof. Kudang B. Seminar, MSc, PhD
e-mail: kseminar@ipb.ac.id
Database sebagai Komponen Vital Sistem
Informasi
E
DA
R
A
TA
W
WA
N
I
A
RE
Performance
R
B
Control System
S
O
Data
F
T
W
A
R
E
Process
Data Store
N E TWAR E
H
A
R
Info
D
W
A
R
E
Data vs Information
Data: raw facts or observations
Information : data that have
been transformed into a
meaningful and useful context
for specific end users
Data
Information
Data Sales person
Sales Values
Sales Units
Data
Processing
Sales Analysis
Sample Business Application
Sample Tabular View of Sales
Sample Pivot Chart for Sale Analysis
Akusisi Data Geografis
Data Geografis Yang Tersimpan
Produk Informasi Geografis
Basis Data (Database)
Koleksi terpadu dari data-data yang saling
berkaitan yang dirancang untuk suatu
enterprise.
Data
Dosen
Data
Mhs
Data
Alumni
Data
Mkul
Analisis Kebutuhan Data
(Data Requirement Analyisis)
• Think and conceptualize business objects and logic
• Identify information needed -> then what data are needed
• Formulate what computer applications are needed?
Dokumentasikan hasil Analisis dengan Alat Bantu
Permodelan (Modeling Tools)
Kasus Contoh: Data Requirement Analysis
Forward Support Analysis
Sources of
Data
Supporting
Data
Supporting
Information
Management
Objectives
Management
Functions
Backward Requirement Analysis
• BAAK
• KRS
• Academic Progress
• Monitoring Student Progress …
• Monitoring
• Faculty
• Transkrip
• Treated Students
• Directing Student Research …
• Directing
• Dept.
• Supervisi
• Student Potentials
• Planning for Remedial Efforts .
• Planning
• Study
Program
• Research
List
• Academic Problem
• Acting on Remedial Plan …
• Acting
Contoh Kasus: Analisis Kebutuhan Data Mhs
Data
Info
KRS, Transkrip
IPK Kumulatif
Status Akademik
Mhs
Warning 1, 2, 3,
rekomendasi
D.O or Extended
Minat riset &
PTA mhs, Data
PTA
Profile minat
riset & PTA
mhs, Beban
PTA
Analisis minat riset
& PTA mhs
Alokasi PTA utk
mhs
Alokasi final PTA
utk mhs
Catatan riset
mhs, Trankrip,
KRS.
Kemajuan riset
mhs
Status Akademik
Mhs
Rekomendasi
perlakuan
Eksekusi
perlakuan
Catatan riset
mhs, Trankrip,
KRS
Profile
kelulusan mhs:
lama studi &
prestasi akad.
Analisis kelulusan:
rerata lama studi,
ranking akademik
Rekomendasi
program
akselerasi studi
Eksekusi
akselerasi studi
Data= Info=
Data1..n
Info1..n
Monitoring
Directing
Acting
Management Functions = Monitoring
Directing Acting Mencapai
Target Academic Excellence?
Utilisasi Vs Ketersedian Informasi
•
•
•
•
Ada dan Diperlukan
Tak ada dan Diperlukan
Ada dan Tak Diperlukan
Tak Ada dan Tak Diperlukan
Perlu
Ada
Tak Ada
Tak Perlu
Data Acquisition &
Information Production
Database Management Systems (DBMS)
Koleksi terpadu dari sekumpulan program (utilitas) yang
digunakan untuk mengakses dan merawat database
Users
DBMS
Database
Utilitas
Application Programs on Top of DBMS
Users
Application programs
DBMS
Database
Keuntungan DBMS
• Data menjadi shareable resources bagi berbagai
user dan aplikasi
• Metoda akses, penggunaan, dan perawatan
data menjadi seragam dan konsisten
• Pengulangan (redundancy) data dan
kemajemukan struktur data diminimisasikan
• Ketaktergantungan data terhadap program
aplikasi (data independence)
• Hubungan/relasi logik (logical relationship)
antar data terpelihara secara sistematik.
Conventional Data Management
Application
Application
• Data belongs to a certain application programs ; therefore it is
difficult to share data among application programs
• Data lifetime is limited (dependent ) to application program lifetime.
• Data redundancy and inconsistency will likely occur
• Non-uniform access method, data usage and maintenance.
• Incompatibility of data among application programs
Examples of software tools in DBMS
• Designing : ERD (Entity Relationship Diagram), DDL (Data
Definition Language)
• Inputing & Manipulating: DML (Data Modification
Language), QL (Query Language), Multimedia processor
• Searching & Retrieving: QL (Query Language): SQL * QBE
• Converting & Squeezing: Encoder & Decoder, Data
Converter & Squeezer, Multimedia processor
• Optimizing : Data Organizer & Analyzer
• Calculating: Math & statistical functions
• Presenting: Report Generator, Multimedia Processor
DBMS Approach Enables Resource Sharing Among
Applications and Users
Multiple Systems
Shareable
Resources
Data Management Life Cycle
• Need of changes
Real World
• Observing
• Identifying
• Updating
• Monitoring
• Protecting
• Browsing
• Conceptualizing
• Representing
• Structuring
• Analyzing
• Optimizing
• Coding
Data Modeling: Methods & Tools
Why Modeling?
Order
“Modeling captures essential
parts of the system.”
Item
Dr. James Rumbaugh
Ship via
Business Process
Visual Modeling is modeling
using standard graphical
notations: chart, diagrams,
objects, symbols
Copyright © 1997 by Rational Software Corporation
Data Model
Definition: Integrated collection of concepts,
theories, axioms, constraints for description,
organization, validation, and interpretation of data.
Usage: a fundamental set of tools & methods to
consistently & uniformly view, organize, and treat
database .
Types Data Models
Record-Based
Model
Relational
Hierarchical
Network
Object-Based
Model
Entity-relationship
Semantic
Functional
Object Oriented
Steps of Designing DBMS
• Determine what to store
• Determine what relations exists
• Determine what data services are needed
• Determine what data model is suitable
Data Warehouse
Kudang B. Seminar
What is Data warehouse?
• Data warehouse as a subject- oriented,
integrated, time variant, non-volatile
collection of data in support of
management’s decision making process
• Data warehouse systems consist of a set
of programs that extract data from the
operational environment, a database
that maintains data warehouse data,
and systems that provide data to users
The Goal of Data Ware
House?
•to provide a "single image
of business reality" for the
organization
Fundamental Ideas Behind the
Successful Data Warehousing
• Operational vs. Decision Support Applications : One impetus for
•
•
•
•
data warehouse is the unsuitability of traditional operational
applications for typical decision support usage patterns;
Primitive vs. Derived Data : A critical success factor in data
warehouse design is understanding knowledge workers’
demand demand for detailed vs. summary data;
Time Series Data: Data warehouse often supports analysis of
trends over time and comparisons of current vs. historical data;
Data Administration: Another critical success factor is senior
management commitment to maintenance of the quality of
corporate data
Systems Architecture: A system must be architected when it is
very complex, requires the integration of many disciplines, or is
developed in the face of uncertain requirements.
Alignment of data warehouse entities with the
business structure
Corporate Data for
Warehouses
A
corporate data warehouse is a
process by which related data
from many operational systems is
merged to provide a single,
integrated business information
view that spans all business
divisions.