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

2. Performance Control System Data Info Process Data Store BRAINWARE DATAWARE HARDWAREHARDWARE SOFTWARESOFTWARE N E T W A R E Database sebagai Komponen Vital Sistem Informasi

3. 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

4. Sample Business Application

5. Sample Tabular View of Sales

6. Sample Pivot Chart for Sale Analysis

7. Akusisi Data Geografis

8. Data Geografis Yang Tersimpan

9. Produk Informasi Geografis

10. Basis Data (Database) Koleksi terpadu dari data-data yang saling berkaitan yang dirancang untuk suatu enterprise. Data Mhs Data Dosen Data Mkul Data Alumni

11. 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?

12. Dokumentasikan hasil Analisis dengan Alat Bantu Permodelan (Modeling Tools)

13. 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

14. DataInfoMonitoringDirectingActing KRS, TranskripIPK KumulatifStatus 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=  Data 1..n  Info=  Info 1..n  Management Functions =  Monitoring   Directing   Acting  Mencapai Target Academic Excellence? Contoh Kasus: Analisis Kebutuhan Data Mhs

15. 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

16. Data Acquisition & Information Production

17. Database Management Systems (DBMS) Koleksi terpadu dari sekumpulan program (utilitas) yang digunakan untuk mengakses dan merawat database Database DBMS Utilitas Users

18. Application Programs on Top of DBMS Database DBMS Application programs Users

19. 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.

20. 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

21. 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

22. Multiple Systems Shareable Resources DBMS Approach Enables Resource Sharing Among Applications and Users

23. Data Management Life Cycle Real World Observing Observing Identifying Identifying Conceptualizing Conceptualizing Representing Representing Structuring Structuring Coding Coding Optimizing Optimizing Analyzing Analyzing Updating Updating Protecting Protecting Monitoring Monitoring Browsing Browsing Need of changes Need of changes

24. Data Modeling: Methods & Tools

25. 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 modeling using standard graphical notations: chart, diagrams, objects, symbols Why Modeling?

26. 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.

27. Types Data Models n Entity-relationship n Semantic n Functional n Object Oriented Object-Based Model n Relational n Hierarchical n Network Record-Based Model

28. Steps of Designing DBMS Determine what to store Determine what relations exists Determine what data services are needed Determine what data model is suitable

29. Data Warehouse Kudang B. Seminar

30. 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 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 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

31. The Goal of Data Ware House? to provide a "single image of business reality" for the organization to provide a "single image of business reality" for the organization

32. 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; 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 demand for detailed vs. summary data; 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; 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 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. 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.

33. Alignment of data warehouse entities with the business structure

34. 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