| dc.contributor | 圖檔所 | - |
| dc.creator (作者) | 李沛錞 | - |
| dc.creator (作者) | Lee, Pei-Chun;Lin, Tzu-Yu | - |
| dc.date (日期) | 2025-10 | - |
| dc.date.accessioned | 13-Nov-2025 10:40:42 (UTC+8) | - |
| dc.date.available | 13-Nov-2025 10:40:42 (UTC+8) | - |
| dc.date.issued (上傳時間) | 13-Nov-2025 10:40:42 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=179688 | - |
| dc.description.abstract (摘要) | In rapidly evolving scientific domains such as edge computing, timely identification of emerging research directions is increasingly critical. Preprints, as fast and openly accessible scholarly outputs, have long served as an important medium for the early dissemination of scholarly knowledge, particularly in high-velocity fields. However, their lack of peer review and heterogeneous quality present challenges for systematic analysis. This study examines edge computing preprints published between 2021 and 2023, retrieved from the Web of Science Preprint Citation Index. A hybrid topic modeling approach combining Latent Dirichlet Allocation (LDA) and Word2Vec-based semantic similarity was applied, along with an institutional credibility–based filtering mechanism to ensure corpus quality. The findings reveal three prominent thematic clusters: (1) resource management and task scheduling, focusing on optimizing computational infrastructure; (2) edge intelligence and federated learning, highlighting distributed AI applications; and (3) real-time analytics and low-latency processing, reflecting network efficiency concerns. These results align with prior research and suggest that preprints can complement traditional literature in capturing early research signals. The proposed framework demonstrates adaptability to dynamic, unstructured corpora and offers a scalable method for real-time knowledge monitoring in emerging technological fields. | - |
| dc.format.extent | 106 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Scientometrics, Vol.130, pp.5547–5569 | - |
| dc.subject (關鍵詞) | Preprints; Topic modeling; Latent Dirichlet allocation; Word embedding; Edge computing | - |
| dc.title (題名) | Time-sensitive research through preprints: employing topic modeling based on correlation and significance | - |
| dc.type (資料類型) | article | - |
| dc.identifier.doi (DOI) | 10.1007/s11192-025-05437-5 | - |
| dc.doi.uri (DOI) | https://doi.org/10.1007/s11192-025-05437-5 | - |
