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DATABASE MANAGEMENT DATABASE MANAGEMENT
SYSTEMS (DBMS)SYSTEMS (DBMS)
by
Prof. Kudang B. Seminar, MSc, PhD
e-mail: [email protected]
Basis Data (Database)
Koleksi terpadu dari data-data yang saling
berkaitan yang dirancang untuk suatu enterprise.
DataData
MhsMhs
Data Data
DosenDosen
Data Data
MkulMkul
Data Data
AlumniAlumni
2
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 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
3
DataData InfoInfo MonitoringMonitoring DirectingDirecting ActingActing
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 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
4
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
5
Tim Pengembangan Master Plan
Eksplorasi 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.
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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
7
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
8
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?
9
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
EntityEntity--relationshiprelationship
SemanticSemantic
FunctionalFunctional
Object OrientedObject Oriented
ObjectObject--Based Based
ModelModel
Relational Relational
HierarchicalHierarchical
NetworkNetwork
RecordRecord--Based Based
ModelModel
10
Data WarehouseData Warehouse
Kudang B. SeminarKudang B. Seminar
What is Data warehouse?What is Data warehouse?
•• Data warehouse as a subjectData warehouse as a subject-- oriented, oriented, integrated, time variant, nonintegrated, time variant, non--volatile 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 set Data warehouse systems consist of a set of programs that extract data from the of programs that extract data from the operational environment, a database that operational environment, a database that maintains data warehousemaintains data warehouse data, and data, and systems that provide data to userssystems that provide data to users
11
The Goal of Data Ware House?The Goal of Data Ware House?
•• to provide a "to provide a "single image of single image of business realitybusiness 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 operationalapplications 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 data;trends over time and comparisons of current vs. historical 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 is A system must be architected when it is very complex, requires the integration of many disciplines, or is very complex, requires the integration of many disciplines, or is developed in the face of uncertain requirements.developed in the face of uncertain requirements.
12
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 from process by which related data from many operational systems is merged to many operational systems is merged to provide a single, integrated business provide a single, integrated business information view that spans all information view that spans all
business divisions.business divisions.
Corporate Data for WarehousesCorporate Data for Warehouses