DATABASE MANAGEMENT SYSTEMS DBMS by Prof Kudang B

DATABASE MANAGEMENT SYSTEMS (DBMS) by Prof. Kudang B. Seminar, MSc, Ph. D e-mail: kseminar@ipb. ac. id

Database sebagai Komponen Vital Sistem Informasi W N I A R E R A B S O Data F T W A R E DA T Performance Control System Process Data Store NETWARE AW AR 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 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= Data 1. . n Info 1. . 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 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 • 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. ” Dr. James Rumbaugh Item 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 n n n Relational Hierarchical Network Object-Based Model n n 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 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.