Background: With the rapid progress in the world, it is still a drawback that data modeling have to be developed and maintained separately each time when enhanced work is carried out. The technology we have now is not good enough to automate itself and keep itself up to date. It needs regularly to get help from people to keep working. In addition, a lot of storage space is needed to hold the new design and data because a single source cannot manage it. Every organization has to deal with this problem. The majority of large, dispersed organizations are most affected. Companies have a lot of data to handle and keep track of, so the data modeling process should be quick, error-free, and not risky in any way.

Objectives: To get around these problems, there should be a way to automate the data modeling. The aim of this thesis is to give a way to solve the problem through automation. The outcome of the research not only helps to automate the data modeling, but it also keeps the data in a way that doesn’t waste memory on unnecessary data.

Methods: This research employs a case study and a literature review. The case study was done at Jeppesen on the Dave modeling language. A literature review was undertaken in order to employ a specific approach for extending the data model. A survey is carried out to identify Dave’s limitations and considerations for improvement. The replies are supplied by Jeppesen employees (developers and users of Dave teams). Research was conducted and the findings were implemented as a program to upgrade the Dave modeling language to meet new object-oriented demands.

Results: The findings identify some limitations of the existing Dave language and present an approach for automating the data modeling abilities by incorporating new features such as abstraction and inheritance so that it can keep up with the real time environments. It creates and automates the data model to provide rapid and right standard. The implementation strategy is drawn from the findings of a literature review.

Conclusions: The existing data model requires extensive manual labour. By adding abstraction and inheritance to the data model, the new data model automates the process, reduces staffing needs, and runs with fewer risks.