Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach
Modeling complex data, e.g. time series as well as network-based data, is a prominent area of research. In this paper, we focus on a combination of both, analyzing network-based spatial sensor data which is attributed with high frequency time series information. We apply a symbolic representation an...
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| Language: | English |
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LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133109 |
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| author | Leonid Schwenke Stefan Bloemheuvel Martin Atzmueller |
| author_facet | Leonid Schwenke Stefan Bloemheuvel Martin Atzmueller |
| author_sort | Leonid Schwenke |
| collection | DOAJ |
| description | Modeling complex data, e.g. time series as well as network-based data, is a prominent area of research. In this paper, we focus on a combination of both, analyzing network-based spatial sensor data which is attributed with high frequency time series information. We apply a symbolic representation and an attention-based local abstraction approach, to enhance interpretability on the respective complex high frequency time series data. For this, we aim at identifying informative measurements captured by the respective nodes of the sensor network. To do so, we demonstrate the efficacy of the Symbolic Fourier Approximation (SFA) and the attention-based symbolic abstraction method to localize relevant node sensor-information, by using a transformer architecture as an encoder for a graph neural network. In our experiments, we compare two seismological datasets to their previous state-of-the-art model, demonstrating the advantages and benefits of our presented approach. |
| format | Article |
| id | doaj-art-0bc91d6f67ba467fadff911d8227b3ff |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-0bc91d6f67ba467fadff911d8227b3ff2025-08-20T03:05:35ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13310969415Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling ApproachLeonid Schwenke0https://orcid.org/0000-0002-2337-3905Stefan Bloemheuvel1Martin Atzmueller2https://orcid.org/0000-0002-2480-6901Osnabrück UniversityJheronimus Academy of Data ScienceOsnabr¨uck University & DFKIModeling complex data, e.g. time series as well as network-based data, is a prominent area of research. In this paper, we focus on a combination of both, analyzing network-based spatial sensor data which is attributed with high frequency time series information. We apply a symbolic representation and an attention-based local abstraction approach, to enhance interpretability on the respective complex high frequency time series data. For this, we aim at identifying informative measurements captured by the respective nodes of the sensor network. To do so, we demonstrate the efficacy of the Symbolic Fourier Approximation (SFA) and the attention-based symbolic abstraction method to localize relevant node sensor-information, by using a transformer architecture as an encoder for a graph neural network. In our experiments, we compare two seismological datasets to their previous state-of-the-art model, demonstrating the advantages and benefits of our presented approach.https://journals.flvc.org/FLAIRS/article/view/133109symbolic time series analysisgraph neural networktransformerattentioninterpretabilitylocal data reductionspatial dataseismic network |
| spellingShingle | Leonid Schwenke Stefan Bloemheuvel Martin Atzmueller Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach Proceedings of the International Florida Artificial Intelligence Research Society Conference symbolic time series analysis graph neural network transformer attention interpretability local data reduction spatial data seismic network |
| title | Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach |
| title_full | Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach |
| title_fullStr | Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach |
| title_full_unstemmed | Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach |
| title_short | Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach |
| title_sort | identifying informative nodes in attributed spatial sensor networks using attention for symbolic abstraction in a gnn based modeling approach |
| topic | symbolic time series analysis graph neural network transformer attention interpretability local data reduction spatial data seismic network |
| url | https://journals.flvc.org/FLAIRS/article/view/133109 |
| work_keys_str_mv | AT leonidschwenke identifyinginformativenodesinattributedspatialsensornetworksusingattentionforsymbolicabstractioninagnnbasedmodelingapproach AT stefanbloemheuvel identifyinginformativenodesinattributedspatialsensornetworksusingattentionforsymbolicabstractioninagnnbasedmodelingapproach AT martinatzmueller identifyinginformativenodesinattributedspatialsensornetworksusingattentionforsymbolicabstractioninagnnbasedmodelingapproach |