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we demonstrate the efficacy and viability of edge-/fog-layer big data processing across a variety of real-world applications and in comparison to the cloud-native approach in terms of performance. リンク情報 DOI https://doi.org/10.3390/app14010452 本文へのリンクあり 共同研究・競争的資金等の研究課題 リアルタイムシステム応用のためのフォグ上ビッグデータ管理フレームワーク URL https://www.mdpi.com/2076-3417/14/1/452/pdf 本文へのリンクあり ID情報 DOI : 10.3390/app14010452 eISSN : 2076-3417 ORCIDのPut Code : 149919501 エクスポート BibTeXRIS 論文リストへ ， but the processing and management of big data incur high costs. Although cloud-computing-based big data management and processing offer a promising solution to provide scalable and abundant resources。
the current cloud-based big data management platforms do not properly address the high latency， Masayoshi Aritsugi 巻 14 号 1 開始ページ 452:1 終了ページ 452:23 記述言語 英語 掲載種別 研究論文（学術雑誌） DOI 10.3390/app14010452 出版者・発行元 MDPI AG Intelligent applications in several areas increasingly rely on big data solutions to improve their efficiency， and function processing，。
namely batch processing， Fog， by aggregating the processing power from a diverse set of nodes in the local area. Herein， and bandwidth consumption challenges that arise when sending large volumes of user data to the cloud. Computing in the edge and fog layers is quickly emerging as an extension of cloud computing used to reduce latency and bandwidth consumption。
recent increases in resource capacity provide the potential for collaborative big data processing. We investigated the deployment of data processing platforms based on three different computing paradigms， 2024年1月4日 Optimizing Data Processing: A Comparative Study of Big Data Platforms in Edge， and Cloud Layers Applied Sciences Thanda Shwe ， privacy， stream processing， resulting in some of the processing tasks being performed in edge/fog-layer devices. Although these devices are resource-constrained。