# AMANDA: A Middleware for Automatic Migration between Different Database Paradigms
## 6 Conclusions  
This article presents AMANDA, a middleware to migrate data from a Relational Database Management System (RDBMS) to a graph database by providing flexibility to users in the migration process, with schema.json file, which permits users to choose the whole database or just a part of it to be migrated. Furthermore, the AMANDA middleware provides an architecture that can be expanded to support other RDBMSs and graph databases. The experiments were performed with MySQL and Postgres, both RDBMSs, as source databases, and Dgraph as the target database, for the Northwind dataset.  
For the IMDB dataset, with 174,728,704 tuples, the experiment was performed only on Postgres as the source database. The migration time between Postgres and Dgraph took 168 h. On the other hand, for Northwind, with 1107 tuples, it took 1 s to migrate from Postgres do Dgraph.  
The main contributions of AMANDA are (a) its flexibility in allowing the users to define specific tables and attributes to migrate, not requiring the migration of all of the databases, as in the previous works; (b) its adaptability and integration to any database based on RDBMS and non-relational paradigms, such as a graph database, and (c) its direct query use mode, which does not require additional packages or other specific knowledge, facilitating developer use; (d) a high (100%) reliability and excellent execution time (1s) in \(n\) data migration, surpassing related works.  
In future work, we envision: (a) that the computational performance evaluation uses large complex databases composed of images, videos, texts, and tests with memory and CPU and parallel environments; (b) an exploration of a machine-learning or complex-networks-based approach to detect the similarity of the data storage and group the new data considering the migration time characteristics, allowing for the best performances of information retrieval.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Northwind** & \multicolumn{4}{c}{**IMDB**} \\ \hline
**Work** & **Time** & **RAM** & **CPU** & **Time** & **RAM** & **CPU** \\ \hline AMANDA & 1 (s) & 19.47 & 0.54 & 168 (h) & 19.47 & 26.65 \\ \hline [PERSON] et al. & 26.10 (s) & NP & NP & 992 (h) & NP & NP \\ \hline [PERSON] et al. & NP & NP & NP & NP & NP & NP \\ \hline [PERSON] et al. & NP & NP & NP & N/A & N/A & N/A \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of the migration performance. NP: not provided. [PERSON] did not use IMDB; therefore, N/A.  
**Author Contributions:** Conceptualization, P.M.F., T.A.F. and F.T.G.; methodology, T.A.F. and F.T.G.; validation, J.S.Q., F.L.S., T.B.F.S. and P.V.V.P.C.; writing--original draft preparation, P.V.V.P.C., T.A.F., P.M.F., F.T.G., T.B.F.S. and F.S.S.; writing--review and editing, J.S.Q., F.S.S., F.T.G. and F.L.S.; Investigation, P.M.F., T.A.F., F.L.S., T.B.F.S., J.S.Q., P.V.V.P.C., F.S.S. and F.T.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by Samsung Eletronica da Amazonia Ltda., under the stimulus of the Brazilian Informatics Law n\({}^{\circ}\) 8.387/91.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Northwind dataset can be found in [[https://code.google.com/archive/p/northwindextended/downloads](https://code.google.com/archive/p/northwindextended/downloads)]([https://code.google.com/archive/p/northwindextended/downloads](https://code.google.com/archive/p/northwindextended/downloads)), accessed on 9 May 2022. IMDB dataset can be found in [[https://datasets.imdbws.com/](https://datasets.imdbws.com/)]([https://datasets.imdbws.com/](https://datasets.imdbws.com/)), accessed on 9 May 2022.  
**Conflicts of Interest:** The authors declare no conflict of interest.