论文标题
行条件键gan生成合成关系数据库
Row Conditional-TGAN for generating synthetic relational databases
论文作者
论文摘要
除了重现独立表的表格数据属性外，合成关系数据库还需要对相关表之间的关系进行建模。在本文中，我们提出了一种新型生成对抗网络（GAN）模型，该行条件 - 条件性尾型生成对抗网络（RC-TGAN）扩展了表格GAN，以支持建模和合成关系数据库。 RC-TGAN通过将父行的条件数据纳入子表GAN的设计中，模拟表之间的关系信息。我们进一步扩展了RC-TGAN，以建模祖父母桌子行可能对孙子行产生的影响，以防止当父表的行无法传输此关系信息时，以防止这种联系的损失。与基准系统相比，使用八个实际关系数据库的实验结果显示了合成关系数据库的质量的显着改善，这证明了RC-TGAN在保持原始数据库表之间保持关系中的有效性。
Besides reproducing tabular data properties of standalone tables, synthetic relational databases also require modeling the relationships between related tables. In this paper, we propose the Row Conditional-Tabular Generative Adversarial Network (RC-TGAN), a novel generative adversarial network (GAN) model that extends the tabular GAN to support modeling and synthesizing relational databases. The RC-TGAN models relationship information between tables by incorporating conditional data of parent rows into the design of the child table's GAN. We further extend the RC-TGAN to model the influence that grandparent table rows may have on their grandchild rows, in order to prevent the loss of this connection when the rows of the parent table fail to transfer this relationship information. The experimental results, using eight real relational databases, show significant improvements in the quality of the synthesized relational databases when compared to the benchmark system, demonstrating the effectiveness of the RC-TGAN in preserving relationships between tables of the original database.
