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DATABASE MANAGEMENT

SYSTEMS (DBMS)

by

Prof. Kudang B. Seminar, MSc, PhD

e-mail: kseminar@ipb.ac.id

Performance

Control System

Data InfoProcess

Data Store

N E T W A R E

Database sebagai Komponen Vital Sistem

Informasi

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Data

ProcessingSales 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)

Koleksi terpadu dari data-data yang saling

berkaitan yang dirancang untuk suatu enterprise.

Data

Mhs

Data

Dosen

Data

Mkul

Data

Alumni

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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?

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

AdaTak 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

DBMSUtilitas

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

ShareableResources

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

Data Modeling: Methods & Tools

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Copyright © 1997 by Rational Software Corporation

Business Process

Order

Item

Ship via

“Modeling captures essential parts of the system.”

Dr. James Rumbaugh

Visual Modeling is modelingusing standard graphical notations: chart, diagrams, objects, symbols

Why Modeling?

Data Model

Usage: a fundamental set of tools & methods to

consistently & uniformly view, organize, and treat

database .

Definition: Integrated collection of concepts,

theories, axioms, constraints for description,

organization, validation, and interpretation of data.

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Types Data Models

Entity-relationship

Semantic

Functional

Object Oriented

Object-Based

Model

Relational

Hierarchical

Network

Record-Based

Model

Steps of Designing DBMS

• Determine what to store

• Determine what relations exists

• Determine what data services are needed

• Determine what data model is suitable

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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

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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 operationalapplications 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.

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Alignment of data warehouse entities with the business structure

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.

Corporate Data for Warehouses