title: Using Bellman Optimality Principle for the Generative Autoencoder Architecture for the Problems of the Attribute Data Typesetting and Semantic Description in Data Management
metadata.name: dc.contributor.author
metadata.value: Kuznetsov, Sergey
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-12-02T15:42:22Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-12-02T15:42:22Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024-06
metadata.lang: -
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu35.2024.207
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/48051
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The publication presents the problems of identifying data types (typesetting) and semantic description of the attributes when managing structured data and master data (Master Data Management). A formal definition of the generalized attribute typesetting problem is given, which allows generation of the additional data types. This problem allows using the discrete Bellman optimality principle under special criteria of the target function. A unified architecture of the deep generative neural network addressing simultaneously the generalized attribute typesetting and semantic description generation problems is proposed. The architecture is based on the generative adversarial autoencoder architecture (AAE) using the mechanisms of soft-attention, and long-term memory (SCRN). The effectiveness of such implementation, in particular, is achieved through the application of the principles of dynamic programming within each epoch of the network training.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Differential Equations and Control Processes;Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: generative neural networks
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: metadata
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: data typesetting
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: data types generation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: discrete Bellman optimality principle
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: neural network architecture
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: deep learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: GAN
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: data profiling
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: master data
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: AAE
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: SCRN
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: LSTM
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: memory cell
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: soft attention
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Using Bellman Optimality Principle for the Generative Autoencoder Architecture for the Problems of the Attribute Data Typesetting and Semantic Description in Data Management
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 24207-jdecp-kuznetsov-s.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/48051/1/24207-jdecp-kuznetsov-s.pdf
resources.format: Adobe PDF
resources.size: 629,43 kB