DATABASE MANAGEMENT SYSTEMS (DBMS) fileData vs Information Data: raw facts or observations...

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1 DATABASE MANAGEMENT SYSTEMS (DBMS) by Prof. Kudang B. Seminar, MSc, PhD e-mail: [email protected] Performance Control System Data Info Process Data Store N E T W A R E Database sebagai Komponen Vital Sistem Informasi

Transcript of DATABASE MANAGEMENT SYSTEMS (DBMS) fileData vs Information Data: raw facts or observations...

Page 1: DATABASE MANAGEMENT SYSTEMS (DBMS) fileData vs Information Data: raw facts or observations Information : data that have been transformed into a meaningful and useful context for specific

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DATABASE MANAGEMENT

SYSTEMS (DBMS)

by

Prof. Kudang B. Seminar, MSc, PhD

e-mail: [email protected]

Performance

Control System

Data Info Process

Data Store

N E T W A R E

Database sebagai Komponen Vital Sistem

Informasi

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Data

Processing Sales Analysis

Data Information

Data Sales person

Sales Values

Sales Units

Data vs Information

Data: raw facts or observations

Information : data that have been transformed into a meaningful and useful context for specific end users

Sample Business Application

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Sample Tabular View of Sales

Sample Pivot Chart for Sale Analysis

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Akusisi Data Geografis

Data Geografis Yang Tersimpan

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Produk Informasi Geografis

Basis Data (Database)

Integrated collection of inter-related data

designed for the need of an enterprise.

Student

Data

Lecturer

Data

Course

Data

Alumni Data

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(Data Requirement Analyisis)

• Think and conceptualize business objects and logic • Identify information needed -> then what data are needed • Formulate what computer applications are needed?

Management

Functions

Management

Objectives

Supporting

Information

Supporting

Data

Sources of

Data

Backward Requirement Analysis

Forward Support Analysis

• Monitoring

• Directing

• Planning

• Acting

• Monitoring Student Progress …

• Directing Student Research …

• Planning for Remedial Efforts .

• Acting on Remedial Plan …

• KRS

• Transkrip

• Supervisi

• Research

List

• Academic Progress

• Treated Students

• Student Potentials

• Academic Problem

• BAAK

• Faculty

• Dept.

• Study

Program

Kasus Contoh: Data Requirement Analysis

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Data Info Monitoring Directing Acting

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=

Data1..n

Info=

Info1..n

Management Functions = Monitoring

Directing Acting Mencapai

Target Academic Excellence?

Contoh Kasus: Analisis Kebutuhan Data Mhs

Utilisasi Vs Ketersedian Informasi

• Ada dan Diperlukan

• Tak ada dan Diperlukan

• Ada dan Tak Diperlukan

• Tak Ada dan Tak Diperlukan

Ada Tak Ada

Perlu

Tak Perlu

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Data Acquisition &

Information Production

Database Management Systems (DBMS) Koleksi terpadu dari sekumpulan program (utilitas) yang

digunakan untuk mengakses dan merawat database

Database

DBMS Utilitas

Users

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Application Programs on Top of DBMS

Database

DBMS

Application programs

Users

Tim Pengembangan Master Plan

Eksplorasi Database

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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

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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

Multiple Systems

Shareable Resources

DBMS Approach Enables Resource Sharing Among

Applications and Users

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Data Management Life Cycle

Real World

• Observing • Identifying

• Conceptualizing • Representing • Structuring

• Coding

• Optimizing • Analyzing • Updating

• Protecting • Monitoring

• Browsing

• Need of changes