The Use of Graph Databases for Artificial Neural Networks
Storing and using trained artificial neural network (ANN) models face technical difficulties. These models are usually stored as files and cannot be run directly. An artificial neural network can be structurally expressed as a graph. Therefore, it would be much more useful to store ANN models in a d...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Çanakkale Onsekiz Mart University
2021-03-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/1615834 |
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| Summary: | Storing and using trained artificial neural network (ANN) models face technical difficulties. These models are usually stored as files and cannot be run directly. An artificial neural network can be structurally expressed as a graph. Therefore, it would be much more useful to store ANN models in a database and use the graph database as this database system. In this study, training and testing stages of ANN models are provided with software that will allow multiple researchers to conduct joint research on ANN models. The developed software platform is aimed to increase the representation power of the currently used methods by transferring the models developed in the popular ANN frameworks used today. With the study conducted, even someone who has started learning artificial neural network models from scratch will see the process and can visually develop their own model. When models are stored in the graph database, it will be easier to making versions and observing how the model grows. In addition, data to be input and output to the model can be stored in this database, also. In order to feed ANN models with input data and produce outputs, the graph database's own query language was used. This eliminates the dependency on another software library. |
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| ISSN: | 2757-5195 |