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

6. Sample Tabular View of Sales

7. Sample Pivot Chart for Sale Analysis

8. Akusisi Data Geografis

9. Data Geografis Yang Tersimpan

10. Produk Informasi Geografis

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

12. 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?

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

14. 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

15. 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

16. 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

17. Data Acquisition & Information Production

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

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

20. 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.

21. Conventional Data Management Application Data merupakan milik aplikasi tertentu, akses data lintas aplikasi menjadi sulit Umur data (data lifetime) tergantung kepada umur aplikasinya Potensi pengulangan dan inkosistensi data tinggi Metoda akses, penggunaan dan perawatan data cenderung tak seragam Struktur data antar aplikasi berpeluang tidak kompatibel

22. 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

23. Multiple Systems Shareable Resources Pendekatan DBMS memungkinkan berbagi guna sumberdaya (data, utilitas, program) antar sistem aplikasi

24. 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

25. Data Modeling: Methods & Tools

26. 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?

27. Hierarchy of Data Abstractions Hierarchy of Data Abstractions View 1View 2View n … Conceptual schema Internal schema Database External Level Conceptual Level Internal Level Physical Level

28. Tingkatan Abstraksi Data n Extenal Level : describes only part of database relevant to specific users n Conceptual Level : describes “what” to store (entity & attributes), constraints, semantics, data integrity & security, also relationhips among data n Internal Data : describes “how” data is organized & stored (memory allocation, indexing, compressing) n Physical Level : describes file structures comprising database

29. 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.

30. 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

31. Relational Data Model Representation of data as an integrated collections of inter-related tables

32. IDStudentNameIDCourseCode CourseNameCredit MMA.101Rudi Wibowo SIM105MIS3 MMA.101SIM105 MMA.102Melinda AKO104DBMS 3 MMA.101AKO104 MMA.102SIM105 record Field/attribute Samples of Relational Data

33. Hierarchical Data Model Representation of data as a tree structure (one- to-many relationships)

34. Sample of Hierarchical Data Country Province City

35. Network Data Model Representation of data as a network structure (many-to- many relationships)

36. Departmen Employee Research WorkProjects Fund Source Departmen Sample of Network Model

37. Entity Relationship Model n Representation of data as entity, attribute, & relationship n Mainly used for conceptual modeling & designing of database Student Course Instructor Take Teach Supervise Year Grade Code ID StudentID

38. Functional Data Model n Representation of data using logic: predicate logic, proportional logic, & functional logic n Mainly for expert system & Artificial Intelligence (AI) Is-bird (pigeon) Is-bird (?x) -> Has-wings (?x) Has-wings (?y) -> Can-fly (?y) Can-fly (pigeon) Has-wings (pigeon) Conclusion Greater-Than(Body- Temperature-Of (?x)), 37)  Is-human (?x)  Is-Sick (?x) Facts: Rules: Derived Facts:

39. Object-Oriented Data Model Encapsulation of attributes & behaviors Inheritance of object attributes & behaviors : single or multiple inheritance attribute Behavior Object attribute Behavior Object Message Interobject communication by message exchange

40. Sample of Object-Oriented ModelCreature Breathing Breathing Reproducing Reproducing Eating Eating Human IS-A Creature IS-A Creature Intelligent Intelligent Student IS-A Human IS-A Human Enrolled in University Enrolled in University Rudi Wibowo Instance-of Student Instance-of Student Nrp: MMA.101 Nrp: MMA.101 Eli Rosida Instance-of Student Instance-of Student Nrp: MMA.102 Nrp: MMA.102 Animal IS-A Creature IS-A Creature Less Intelligent Less Intelligent Herbivor IS-A Animal IS-A Animal Eats plants Eats plants

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

42. Data Warehouse Kudang B. Seminar

43. What is Data warehouse? Data warehouse is an architecture for organizing IS. Data warehouse is an architecture for organizing IS. 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

44. 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

45. 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.

46. Operational vs Decision Support Systems Operational systems, like generalledger, materials management, or order processing, generally access and update the record of a single business object or event: one account, one inventory item, or one order. Transactions are generally pre-defined, and require the database to provide very fast access one record at a time. Operational systems, like generalledger, materials management, or order processing, generally access and update the record of a single business object or event: one account, one inventory item, or one order. Transactions are generally pre-defined, and require the database to provide very fast access one record at a time. DSS/EIS users are traditionally managers who think about the big picture long term. Databases supporting decision support should be able to retrieve large sets of aggregate and historical data within a reasonable response time. DSS/EIS users are traditionally managers who think about the big picture long term. Databases supporting decision support should be able to retrieve large sets of aggregate and historical data within a reasonable response time. By separating these two very different processing patterns, the data warehouse architecture enables both operational and decision support applications to focus on what they do best and therefore provide better performance and functionality By separating these two very different processing patterns, the data warehouse architecture enables both operational and decision support applications to focus on what they do best and therefore provide better performance and functionality

47. Converting Data for Warehouses

48. Alignment of data warehouse entities with the business structure

49. 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

50. Architecture of Data Warehouse