DATABASE MANAGEMENT DATABASE MANAGEMENT SYSTEMS (DBMS)SYSTEMS (DBMS)

by

Prof. Kudang B. Seminar, MSc, PhD

e-mail: kseminar@ipb.ac.id

Performance Control System

Data InfoProcessProcess

Data StoreData Store

BRAINWARE DATAWARE

HA

RD

WA

RE

SO

FTW

ARE

N E T W A R E

Database sebagai Komponen Vital Sistem Database sebagai Komponen Vital Sistem InformasiInformasi

Data

ProcessingSales Analysis

Data Information

Data Sales person

Sales Values

Sales Units

Data vs InformationData vs Information

DataData: : raw facts or observationsraw facts or observations

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

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.

DataDataMhsMhs

Data Data DosenDosen

Data Data MkulMkul

Data Data AlumniAlumni

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)

Management Functions

Management Objectives

Supporting Information

Supporting Data

Sources of Data

Backward Requirement AnalysisBackward Requirement Analysis

Forward Support AnalysisForward 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: Kasus Contoh: Data Requirement AnalysisData Requirement Analysis

DataData InfoInfo MonitoringMonitoring DirectingDirecting ActingActingKRS, Transkrip IPK Kumulatif Status Akademik

MhsWarning 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 MhsContoh 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

Data Acquisition & Data Acquisition & Information ProductionInformation Production

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

digunakan untuk mengakses dan merawat database

Database

DBMSDBMSUtilitas

UsersUsers

Application Programs on Top of DBMS

Database

DBMSDBMS

Application programs

UsersUsers

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

Multiple Systems

ShareableResources

DBMS Approach Enables Resource Sharing Among Applications and Users

Data Management Life Cycle

Real World

• ObservingObserving• IdentifyingIdentifying

• ConceptualizingConceptualizing• RepresentingRepresenting

• StructuringStructuring

• CodingCoding

• OptimizingOptimizing• AnalyzingAnalyzing• UpdatingUpdating

• ProtectingProtecting• MonitoringMonitoring

• BrowsingBrowsing

• Need of changesNeed of changes

Data Modeling: Methods & Tools

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.

Types Data Models

Entity-relationshipEntity-relationship SemanticSemantic FunctionalFunctional Object OrientedObject Oriented

Object-Based Object-Based ModelModel

Relational Relational HierarchicalHierarchical NetworkNetwork

Record-Based Record-Based ModelModel

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 WarehouseData Warehouse

Kudang B. SeminarKudang B. Seminar

What is Data warehouse?What is Data warehouse?

• Data warehouse as a subject- oriented, Data warehouse as a subject- oriented, integrated, time variant, non-volatile integrated, time variant, non-volatile collection of data in support of collection of data in support of management’s decision making processmanagement’s decision making process

• Data warehouse systems consist of a Data warehouse systems consist of a set of programs that extract data from set of programs that extract data from the operational environment, a the operational environment, a database that maintains data database that maintains data warehousewarehouse data, and systems that data, and systems that provide data to usersprovide data to users

The Goal of Data Ware The Goal of Data Ware House?House?

•to provide a "to provide a "single image single image of business realityof business reality" for the " for the organizationorganization

Fundamental Ideas Behind the Fundamental Ideas Behind the Successful Data WarehousingSuccessful Data Warehousing

• Operational vs. Decision Support ApplicationsOperational vs. Decision Support Applications: : One impetus for One impetus for data warehouse is the unsuitability of traditional operationaldata warehouse is the unsuitability of traditional operational applications for typical decision support usage patterns;applications for typical decision support usage patterns;

• Primitive vs. Derived DataPrimitive vs. Derived Data: A critical success factor in data : A critical success factor in data warehouse design is understanding knowledge workers’ warehouse design is understanding knowledge workers’ demanddemand demand for detailed vs. summary data;demand for detailed vs. summary data;

• Time Series DataTime Series Data: Data warehouse often supports analysis of : Data warehouse often supports analysis of trends over time and comparisons of current vs. historical trends over time and comparisons of current vs. historical data;data;

• Data AdministrationData Administration: Another critical success factor is senior : Another critical success factor is senior management commitment to maintenance of the quality of management commitment to maintenance of the quality of corporate datacorporate data

• Systems ArchitectureSystems Architecture:: A system must be architected when it A system must be architected when it is very complex, requires the integration of many disciplines, is very complex, requires the integration of many disciplines, or is developed in the face of uncertain requirements.or is developed in the face of uncertain requirements.

Alignment of data warehouse entities with the business structure

A A corporate data warehouse is a corporate data warehouse is a process by which related data process by which related data from many operational systems is from many operational systems is merged to provide a single, merged to provide a single, integrated business information integrated business information view that spans all business view that spans all business

divisions.divisions.

Corporate Data for Corporate Data for WarehousesWarehouses