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Home » Professor NL Sarda Discusses Data Modeling – DFD, Function Decomp (Transcript)

Professor NL Sarda Discusses Data Modeling – DFD, Function Decomp (Transcript)

In this module we will talk about data modeling. You will recall that when we introduced software engineering and software development methodology, we were talking about requirement analysis where we mentioned that we perform both the data modeling and the process modeling as we go through the requirement analysis as we understand the application environment of the user.

So let us start by asking again why we model. We build models of complex systems, because it’s difficult to understand any such system in its completeness at one shot. We have to therefore build a model and try to understand it in terms of its complements and how those complements relate with each other. So one of the important reasons for modeling of complex systems is to improve our understanding of such system because we cannot understand them entirely in one single instance.

We need to develop a common understanding of the problem so that we can proceed toward the solution. This common understanding is between all the people involved and also the users for whom we are trying to propose a solution. We cannot afford a trial and error approach. In fact, a model will clearly establish that we are proceeding along the correct direction and that our understanding of the user’s environment is correct. And this will be reflected in the model. This will remove the trial and error kind of approach. And it will also reduce the risk in the overall development.

A model is also extremely useful to communicate the required structure and behavior of our system. We try to capture that in the model and then put it in the form which can be understood and which can be verified by others. So these are the reasons why we model.

Let us see how we model. We choose an appropriate modeling concept or an appropriate modeling paradigm. This should be such that our solution can be properly expressed. So this choice of the right model is extremely important, and it has considerable influence on shaping the solution we propose for the problem. So we chose a model for the kind of purpose we have at hand. This may be modeling of data, or this may be modeling of the processing defined in a given application.

No single model is sufficient. In fact, this is an important point that in most analysis phases, we try to build different types of models which represent the different perspectives of the same environment or the same application. It is important to approach a complex system from different points of view which might be best represented by using different modeling techniques. So a single model may not be sufficient.

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