Literature review

Daily companies based on information technology are facing a lot of issues and computing challenges due to the data management. There is the wide usage of cloud and mobility, and it is opening up new opportunities for that information technology companies to be more service centric and productive. The database is the hidden workhorse that many businesses’ IT systems, storing critical business intelligence and bearing out hundreds to thousand so transactions each day (Cheung et al., 1996). Many companies are storing their information on relational databases, and they anticipate taking advantage of the emerging technologies such as XML or object-based databases. Instead to discarding the previous RDB or building non-relational data on it, it becomes better to convert the old data and applications onto a new environment (Maatuk et al., 2008). However, the question that remains is which of the databases is the most appropriate to migrate? That is the reason there is a need for an integrated method that handles database migration for it to be a success.

The method of migration needs to assist in the selection of the most suitable databases when comparing the databases that are available in the market. It should allow the development of the required applications to provide the required functionality and high performance than the previous one (Vitthal et al., 2013). A canonical model can be useful in the intermediate stage of data conversion and schema from the input database to the various output targets. Many researchers have been researching on the database migrations while focusing on different areas. There are many assumptions in most of the literature that restrict the successful migration. For the migration to a new database to be successful, there is a need for further normalization to third normal form (Premerlani & Blaha, 1994). Many of the literature assume key-based conclusion dependency with key attribute consistencies while another assumption is that the design often initial schema is okay. In Kronsys, the migration has to take place seamlessly, and I will have to ensure the application of the best methodology while working with the company’s database managers and data administrators.

In that research, I will prefer the application of an integrated method for database migration that can preserve the semantics and structure of an existing relational database to generate ORDB/XML schemas. The method also provides an effective way of loading data into the target databases without the loss of unnecessary redundancies. The method is superior to many of the approaches proposed in many kinds of literature, and it leads to three types of databases including an object-oriented, relational database, and XML schema. The method also helps to exploit excellent feature provide to the target outputs. Because there is heterogeneity among the three data models provided as outputs, the method makes it possible to develop a canonical data model (Callan, 2006). That canonical data model is useful in bridging the semantic gap that exists between those data models and consequently facilitate the migration process. The canonical data model does preserve and enhance the target database’s integrity y constraints as well as the data semantics that fit in with the target database characteristics.

The integrated method will consist of three phases including semantic enrichment, schema conversion and data conversion (Maatuk et al., 008). The first phase produces a canonical data model that contains much of the relational database’s constraints and data semantics that did not have an explicit expression in it. There is then the mapping of the canonical data model obtained into the target schemas of the second phase. The third phase is where the conversion of the relational database data into its equivalent in the new database takes place. There should be early designing of the system architecture and implementing a prototype to demonstrate that process that should show the success of the whole process before the actual migration takes place. In a nutshell, the integrated database migration project should entail three phases including semantic enrichment, schema translation, and the data conversion (Topor & Tanaka, 1997).

The semantic enrichment of database migration is where the analysis of the RDB to understand its meaning and structure takes place. That means you make the hidden semantics explicit. For Kronsys, the semantics enrichment phase will involve the extraction of data semantics from the old database and then representing in relational schema format. After that, there will be the conversion of the data into a much enriched canonical model. The schema translation will involve the translation of the canonical model produced in the previous phase into its equivalent target schema (Maatuk et al., 2007). There is following of the necessary rules during the translation that are not in the scope of this paper. The data conversion as the last phase will entail the conversion of the existing database data onto the format required by the target schema. Te canonical data model created in the previous phase is useful in guiding the conversion process.

Proposal

The paper focuses on tracking the internship at Kronsys Inc. in database migration so as to ensure that the migration process takes place seamlessly, and there is no disruption of the business processes. During my internship, I will ensure that the migration task goes on according to the requirements of the business. I will also be in the requirement to explore various modules of the new database and integrate them to benefit the company business operations. While carrying out an internship in database migration at Kronsys Inc, the internship consists of various phases like orientation, training, gathering or understanding requirements, and testing and implements the changes. These phases have explanation below through iterations.

Iteration 1 Orientation

The orientation session helps in understanding the company and their culture. During orientation at Kronsys Inc., the primary goal will be to explore the company and understand its vision and the mission statement. I will also have a good understanding of the focus and key players of the company.

Iteration 2 Training

In this phase, Training on the database migration essentials and operations will have an explanation so as to understand clearly my role in the entire process. During training, there will be a brief introduction to the ethics and standards of the organization plus the provision of real time examples to work on before actually project assignment.

Iteration 3 gathering requirements

After the training, I will analyze the project and obtain a proper understanding of the requirements. There will be the understanding of the project and requirements for the changes through the data gathering process and after analyzing the requirements, I will identify changes and perform analysis on the proposed changes. Later in this phase, there will be making a decision on the changes and obtaining of approvals.

Iteration 4 Test and Implementation

In this phase, there will be finalizing of the database changes and applying them to the existing system and then proper testing of the new database will follow to check the functionality is working properly. The testing will also serve the purpose of finding out if other services are effective because of the change implemented.

References

Callan S. (2006). Database migration—a planned approach. Database Journal.

Cheung, T., Fong, J., & Siu, B. (1996). International Hong Kong Computer Society Database Workshop on Database Reengineering and Interoperability. Database reengineering and interoperability. Boston, MA: Springer.

Maatuk, A. Ali, A. & Rossiter, N. (2008). A framework for relational database migration. Technical report.

Maatuk, A., Ali, A. & Rossiter, N. (2007). A framework for relational database migration.

Maatuk, A., Ali, A. & Rossiter, N. (2008). An integrated approach to relational database migration.

Premerlani, W. J. & Blaha, M. R. (1994). An Approach for Reverse Engineering of Relational Databases. Communication ACM, 37, 42–49.

Topor, R., & Tanaka, K. (1997). Database systems for advanced applications. '97: Proceedings of the fifth International Conference on Database Systems for Advanced Applications, Melbourne, Australia, April 1-4, 1997. Singapore: World Scientific.

Vitthal, S., Baban, T., warade, R. & Chaudhari, K. (2013). Data migration in heterogeneous database. International journal of engineering science and innovative technology, 2(2).

Zhang, X., Zhang, Y., Fong, J. & Jia, X. (1999). Transforming RDB Schema to well structured OODB Schema. Information & Software Technology, 41, 275–281.

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