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Management Information Systems

Management Information Systems. Prof. Payam Hanafizadeh, PhD Allameh Tabataba'i University 2009. Part2: Business Information Systems Development.

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Management Information Systems

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  1. Management Information Systems Prof. Payam Hanafizadeh, PhD Allameh Tabataba'i University 2009

  2. Part2:Business Information Systems Development

  3. A systemanalyst looks at a phenomenon as a system, so s/he has to find out its essiential property and then figure out its components as subsystems. • System analysis makes it possible to understand problems and propose solutions. System Analysis

  4. Complex Problem Complex Solution Problem A Analysis Synthesis Sub-Prob. C Solution A Sub-Prob. B Sub-Sub-Prob. F Sub-Sub-Prob. E Sol. C Sol. B Sol. F Sol. E Sub-Prob. D Sol. D Problem Structure Solution Components Prob. A Sol. A Sub-Prob. B Sub-Prob. D Sol. B Sub-Prob. C Sol. B Sol. C Sol. D Sol. A Sol. F Individual Solution Sol. D Sub-Sub-Prob. E Sub-Sub-Prob. F Sol. E Sol. F Solution Structured Sol. E Sol. C Fig1: The analysis and synthesis of a problem

  5. Existing System New System Physical Model Physical Model Technical & Operational Requirements Business Requirements Logical Model Logical Model Fig2: The Analysis Process Diagram

  6. 4 1 2 3 Fig3-a: The four-step analysis process viewed as a cyclical process

  7. The analysis is an iterative process. • The four steps which make a cyclical process can be iteratedtogradually increase the analysts' understanding The iterative nature of the analysis process

  8. 4 1 3 2 Fig3-b: The four-step analysis process viewed as a cyclical process

  9. 1 4 2 3 4 2 3 4 2 3 Fig4: The iteration and layering inherent in the analysis

  10. A logical model highlights the data content and handling, regardless of methods used to provide them. • Thus, the logical aspects of a system are those elements that are the same whether the work is done with pencils and paper or by a computer. A Logical Model

  11. By contrast, a physical model tends to identify the aspects of the system that are dependent on: • how the processing is currently or will be done • the people who are involved in the processing • the forms used • the computerized processing • and so on. • A model is not necessarily completely physical or compeletely logical. A Physical Model

  12. Table1: Summary of key differences between physical and logical models

  13. Fig5-a: Logical model of an existing system

  14. Fig5-b: Logical model of an existing system- As modified by business requirements Change data Add data Add process Add storage Delete Delete data

  15. Fig5-c: Logical model of new system-derived from logical model of existing system and new business requirements

  16. Batch Fig5-d: Physical model of new system-derived from logical model of new system and new physical (delivery oriented) requirements Man As needed Daily Weekly Machine On-line

  17. It is possible to define a data modeling process that can run in parallel with the construction of physical and logical models and can provide a higher quality new system physical model. • The existing physical data model, constructed in parallel with existing physical function model is simply the list of current physical files-both computer and manual- and their keys. The combination of function (DFD) and data models in the Analysis Process

  18. This will concide with the collection of data store in the function model. • While deriving the logical (function) model for the existing system, also construct an E-R model that spans the existing system. • Next construction the logical data model for the new system. New business requirements may or may not derive the definition of new entities. The combination of function (DFD) and data models in the Analysis Process

  19. But they will almost certainly dictate a change in the relationships and probably in the attributes. • You are now already to merge the two logical models – function and data – into a combined logical model for the new system. The combination of function (DFD) and data models in the Analysis Process

  20. Do this by replacing the data stores that have been carried along in the function or data flow diagram – based models with the normalized set of data stores from the logical data model and adjusting data flows as necessary. • In practise, it is a good idea to do this merge using fairly low-level data flow diagram models, then create the intermediate-level parent diagrams. The combination of function (DFD) and data models in the Analysis Process

  21. It is this merged logical model for the new system that can serve as the base for constructing the physical model for new system. One thing that happens during the construction of this physical model is the identification of the actual physical data bases to be used in new systems. The combination of function (DFD) and data models in the Analysis Process

  22. New System Existing System Data Physical Model Function Technical & Operational Requirements Merge Business Requirements Logical Model Fig6: The combination of function (DFD) and data models in the Analysis Process

  23. Part 2 References [1]: Powers, Cheney, Crow, "Structured Systems Development, Analysis; Design, Implementation", 2nd edition, thompson international publishing, 1990.

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