DeepProbCEP: A neuro-symbolic approach for complex event processing in adversarial settings Article Swipe
Marc Roig Vilamala
,
Tianwei Xing
,
Harrison Taylor
,
Luis Antonio Ribot García
,
Mani Srivastava
,
Lance Kaplan
,
Alun Preece
,
Angelika Kimmig
,
Federico Cerutti
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.eswa.2022.119376
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.eswa.2022.119376
Detecting complex events from subsymbolic data streams (such as images, audio recordings or videos) is a challenging problem, as traditional symbolic approaches cannot be used to process subsymbolic data, and neural-only approaches usually require larger amounts of training data than available. In this paper, we present DeepProbCEP, a Complex Event Processing (CEP) approach designed with four objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining flexibility and modularity in the definition of complex event rules, (iii) limiting the cost of obtaining training data and (iv) being robust against adversarial conditions. DeepProbCEP archives this by using a neuro-symbolic approach, which combines the neural and symbolic approaches to allow training with sparse data. This is made possible through the injection of human knowledge. In this paper, we demonstrate that DeepProbCEP outperforms other state-of-the-art approaches when training using sparse data. We also show that DeepProbCEP is robust in different adversarial settings. Finally, DeepProbCEP’s flexibility is demonstrated by showing it can be used to process both images and audio as input.
Related Topics
Concepts
Computer science
Modularity (biology)
Flexibility (engineering)
Artificial intelligence
Adversarial system
Event (particle physics)
Process (computing)
Machine learning
Artificial neural network
Limiting
Programming language
Genetics
Statistics
Quantum mechanics
Mechanical engineering
Mathematics
Biology
Physics
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.eswa.2022.119376
- https://www.sciencedirect.com/science/article/am/pii/S0957417422023946?via%3Dihub
- OA Status
- hybrid
- Cited By
- 19
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310497342
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310497342Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.eswa.2022.119376Digital Object Identifier
- Title
-
DeepProbCEP: A neuro-symbolic approach for complex event processing in adversarial settingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-01Full publication date if available
- Authors
-
Marc Roig Vilamala, Tianwei Xing, Harrison Taylor, Luis Antonio Ribot García, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico CeruttiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.eswa.2022.119376Publisher landing page
- PDF URL
-
https://www.sciencedirect.com/science/article/am/pii/S0957417422023946?via%3DihubDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.sciencedirect.com/science/article/am/pii/S0957417422023946?via%3DihubDirect OA link when available
- Concepts
-
Computer science, Modularity (biology), Flexibility (engineering), Artificial intelligence, Adversarial system, Event (particle physics), Process (computing), Machine learning, Artificial neural network, Limiting, Programming language, Genetics, Statistics, Quantum mechanics, Mechanical engineering, Mathematics, Biology, Physics, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 7, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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