Structuring the Processing Frameworks for Data Stream Evaluation and Application
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866918150168117248 |
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| author | Komorniczak, Joanna Ksieniewicz, Paweł Zyblewski, Paweł |
| author_facet | Komorniczak, Joanna Ksieniewicz, Paweł Zyblewski, Paweł |
| contents | The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications. The definition of structured frameworks stems from a need to reliably evaluate the data stream classification methods, considering the constraints of delayed and limited label access. The current experimental evaluation often boundlessly exploits the assumption of their complete and immediate access to monitor the recognition quality and to adapt the methods to the changing concepts. The problem is leveraged by reviewing currently described methods and techniques for data stream processing and verifying their outcomes in simulated environment. The effect of the work is a proposed taxonomy of data stream processing frameworks, showing the linkage between drift detection and classification methods considering a natural phenomenon of label delay. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06799 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Structuring the Processing Frameworks for Data Stream Evaluation and Application Komorniczak, Joanna Ksieniewicz, Paweł Zyblewski, Paweł Machine Learning Databases The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications. The definition of structured frameworks stems from a need to reliably evaluate the data stream classification methods, considering the constraints of delayed and limited label access. The current experimental evaluation often boundlessly exploits the assumption of their complete and immediate access to monitor the recognition quality and to adapt the methods to the changing concepts. The problem is leveraged by reviewing currently described methods and techniques for data stream processing and verifying their outcomes in simulated environment. The effect of the work is a proposed taxonomy of data stream processing frameworks, showing the linkage between drift detection and classification methods considering a natural phenomenon of label delay. |
| title | Structuring the Processing Frameworks for Data Stream Evaluation and Application |
| topic | Machine Learning Databases |
| url | https://arxiv.org/abs/2411.06799 |