4f50bedf-f4a7-43d3-bd60-7522feaaf0dd20230208080036719wseas:wseasmdt@crossref.orgMDT DepositInternational Journal of Computational and Applied Mathematics & Computer Science2769-247710.37394/232028https://wseas.com/journals/camcs/index.php62920226292022210.37394/232028.2022.2https://wseas.com/journals/camcs/2022.phpHassan B.HashimMiddle Technical University Baghdad, IRAQIn relational and complex spreadsheets, relational and non-relational database models, high-speed data migration with scalable structure using visual Basic Excel programming language implementations with proposed migration model. One of the primary purposes behind making a point of interaction through the Visual Basic Application (VBA) is that most PC clients with logical preparation will currently know about Succeed and its significant capabilities (like arranging furthermore, plotting datasets). Even though Excel itself is in many cases utilized as an information storehouse by clients. The time factor, reliability, and credibility of migrating this data from one table to another through programming interfaces were measured using the link codes between the tables. In this paper, the migration and migration of homogeneous and heterogeneous data are investigated by using two types of different migration models of data to measure and match these data and the extent of their integration after migration. Specifies the target data for migration from the input tables in the Excel program to the target tables in larger databases. Furthermore, the two models (A, B) middleware provide an architecture that can be extended to support Relational database management systems (RDBMS) and other graphing databases. Experiments were performed using excel tables, both of which are related as source information bases, and as the data set for the source and target datasets, the migration time between these tables for the two models was calculated While retaining the same characteristics.123120221231202213113918https://wseas.com/journals/camcs/2022/a36camcs-018(2022).pdf10.37394/232028.2022.2.18https://wseas.com/journals/camcs/2022/a36camcs-018(2022).pdf10.1088/1757-899x/1099/1/012082Rao, G. M., Srinivas, K., Samee, S., Venkatesh, K., Dadheech, P., Raja, L., & Yagnik, G. (2021, March). A Secure and Efficient Data Migration Over Cloud Computing. In IOP Conference Series: Materials Science and Engineering (Vol. 1099, No. 1, p. 012082). IOP Publishing 10.1002/widm.1407Bonfitto, S., Casiraghi, E., & Mesiti, M. (2021). Table understanding approaches for extracting knowledge from heterogeneous tables. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(4), e1407. 10.3390/info13090424Tu, Y., Tang, H., Gong, H., & Hu, W. (2022). A Flexible Data Evaluation System for Improving the Quality and Efficiency of Laboratory Analysis and Testing. Information, 13(9), 42. 10.1007/s42979-022-01298-4Thirumahal, R., Sudha Sadasivam, G., & Shruti, P. (2022). Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19. SN Computer Science, 3(6), 1-13. Gao, C. (2019). Research on the Application of Big Data in Security Information Collection. 10.1007/s10953-019-00871-5Rowland, D., & May, P. M. (2019). Progress in Aqueous Solution Modeling: Better Data and Better Interfaces. Journal of Solution Chemistry, 48(7), 1066-1078. 10.1007/s10586-021-03461-7Aruna, M. G., Hasan, M. K., Islam, S., Mohan, K. G., Sharan, P., & Hassan, R. (2022). Cloud-to-cloud data migration using self-sovereign identity for 5G and beyond. Cluster computing, 25(4), 2317-2331. 10.3390/bdcc5020024Azeroual, O., & Jha, M. (2021). Without data quality, there is no data migration. Big Data and Cognitive Computing, 5(2), 24. 10.1007/s10619-021-07334-1Hillenbrand, A., Störl, U., Nabiyev, S., & Klettke, M. (2022). Self-adapting data migration in the context of schema evolution in NoSQL databases. Distributed and Parallel Databases, 40(1), 5-25. 10.1109/icaccs51430.2021.9441840Saranya, N., Brindha, R., Aishwariya, N., Kokila, R., Matheswaran, P., & Poongavi, P. (2021, March). Data Migration using ETL Workflow. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 1661-1664). IEEE. 10.1109/csci54926.2021.00068Liu, F. C., Weiner, M. D., Manalo, K., Jezghani, A., Blanton, C. J., Stone, C., ... & Lara, R. (2021, December). Human-in-the-Loop Automatic Data Migration for a Large Research Computing Data Center. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1752-1758). IEEE. 10.1007/s10586-021-03379-0Yuan, Z., You, X., Lv, X., Li, M., & Xie, P. (2021). HDS: optimizing data migration and parity update to realize RAID-6 scaling for HDP. Cluster Computing, 24(4), 3815-3835. 10.4236/jis.2021.121004Hussein, A. A. (2021). Data Migration Need, Strategy, Challenges, Methodology, Categories, Risks, use with Cloud Computing, and Improvements in Its Using with Cloud Using Suggested Proposed Model (DMing 1). Journal of Information Security, 12(01), 79. Luo, J., Li, X., Feng, Y., & Wang, L. (2022, April). Research on Web Big Data Migration Algorithm base 10.1007/s11227-022-04662-6Kaur, K., Bharaniy, S., Bharania, S., Aggarwal, K., Nayyar, A., & Sharma, S. (2022). Energyefficient polyglot persistence database lives migration among heterogeneous clouds. The Journal of Supercomputing, 1-30. 10.1145/3448016.3452827Lin, Y. S., Tsai, C., Lin, T. Y., Chang, Y. S., & Wu, S. H. (2021, June). Don't Look Back, Look into the Future: Prescient Data Partitioning and Migration for Deterministic Database Systems. In Proceedings of the 2021 International Conference on Management of Data (pp. 1156-1168). 10.3390/app12126106Queiroz, J. S., Falcão, T. A., Furtado, P. M., Soares, F. L., Souza, T. B. F., Cleis, P. V. V., ... & Giuntini, F. T. (2022). AMANDA: A Middleware for Automatic Migration between Different Database Paradigms. Applied Sciences, 12(12), 6106. 10.1016/j.regsciurbeco.2021.103714Kirchberger, M. (2021). Measuring internal migration. Regional Science and Urban Economics, 91, 103714. 10.1007/s41060-020-00213-5Sîrbu, A., Andrienko, G., Andrienko, N., Boldrini, C., Conti, M., Giannotti, F., ... & Sharma, R. (2021). Human migration: the big data perspective. International Journal of Data Science and Analytics, 11(4), 341-360. 10.3390/rs13204169Fekri, E., Latifi, H., Amani, M., & Zobeidinezhad, A. (2021). A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine. Remote Sensing, 13(20), 4169. 10.1007/978-3-030-94191-8_64Dourhri, A., Hanine, M., & Ouahmane, H. (2021, November). A New Algorithm for Data Migration from a Relational to a NoSQL Oriented Column Database. In The Proceedings of the International Conference on Smart City Applications (pp. 795-814). Springer, Cham. 10.18034/ei.v9i1.529Amin, R., Vadlamudi, S., & Rahaman, M. M. (2021). Opportunities and challenges of data migration in the cloud. Engineering International, 9(1), 41-50. 10.30837/itssi.2022.20.064Peretiatko, M., Shirokopetleva, M., & Lesna, N. (2022). Research of Methods to Support Data Migration Between Relational and Document Data Storage Models. Innovative Technologies and Scientific Solutions for Industries, (2 (20)), 64-74. 10.1016/b978-0-12-823395-5.00003-3Kumar, A., Dadheech, P., Singh, V., & Raja, L. (2021). Performance modeling for secure migration processes of legacy systems to cloud computing. In Data Deduplication Approaches (pp. 255-279). Academic Press. 10.23919/fruct50888.2021.9347581Ceresnak, R., Matiasko, K., & Dudas, A. (2021, January). Influencing migration processes by realtime data. In 2021 28th Conference of Open Innovations Association (FRUCT) (pp. 1-7). IEEE. 10.1007/978-3-030-89159-6_12Altendeitering, M. (2021, October). Mining data quality rules for data migrations: a case study on material master data. In International Symposium on Leveraging Applications of Formal Methods (pp. 178-191). Springer, Cham. Shin, H. (2021). A Study on Data Migration of Legacy Information System.