A Methodology and System For Big-Thick Data Collection
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913408823066624 |
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| author | Kayongo, Ivan Zhao, Haonan Malcotti, Leonardo Giunchiglia, Fausto |
| author_facet | Kayongo, Ivan Zhao, Haonan Malcotti, Leonardo Giunchiglia, Fausto |
| contents | Pervasive sensors have become essential in research for gathering real-world data. However, current studies often focus solely on objective data, neglecting subjective human contributions. We introduce an approach and system for collecting big-thick data, combining extensive sensor data (big data) with qualitative human feedback (thick data). This fusion enables effective collaboration between humans and machines, allowing machine learning to benefit from human behavior and interpretations. Emphasizing data quality, our system incorporates continuous monitoring and adaptive learning mechanisms to optimize data collection timing and context, ensuring relevance, accuracy, and reliability. The system comprises three key components: a) a tool for collecting sensor data and user feedback, b) components for experiment planning and execution monitoring, and c) a machine-learning component that enhances human-machine interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17602 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Methodology and System For Big-Thick Data Collection Kayongo, Ivan Zhao, Haonan Malcotti, Leonardo Giunchiglia, Fausto Human-Computer Interaction Pervasive sensors have become essential in research for gathering real-world data. However, current studies often focus solely on objective data, neglecting subjective human contributions. We introduce an approach and system for collecting big-thick data, combining extensive sensor data (big data) with qualitative human feedback (thick data). This fusion enables effective collaboration between humans and machines, allowing machine learning to benefit from human behavior and interpretations. Emphasizing data quality, our system incorporates continuous monitoring and adaptive learning mechanisms to optimize data collection timing and context, ensuring relevance, accuracy, and reliability. The system comprises three key components: a) a tool for collecting sensor data and user feedback, b) components for experiment planning and execution monitoring, and c) a machine-learning component that enhances human-machine interaction. |
| title | A Methodology and System For Big-Thick Data Collection |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.17602 |