Big Data Analytics in Astronomy, Science, and Engineering 10th International Conference on Big Data Analytics, BDA 2022, Aizu, Japan, December 5–7, 2022, Proceedings /
Big Data Analytics in Astronomy, Science, and Engineering 10th International Conference on Big Data Analytics, BDA 2022, Aizu, Japan, December 5–7, 2022, Proceedings / [electronic resource] :
edited by Shelly Sachdeva, Yutaka Watanobe, Subhash Bhalla.
- 1st ed. 2023.
- XII, 246 p. 136 illus., 112 illus. in color. online resource.
- Lecture Notes in Computer Science, 13830 1611-3349 ; .
- Lecture Notes in Computer Science, 13830 .
Data Science: Systems -- Architectures -- Big Data Analytics in Healthcare Support Systems -- Information Interchange of Web Data Resources -- Business Analytics.
This book constitutes the proceedings of the 10th International Conference on Big Data Analytics, BDA 2022, which took place in a hybrid mode during December 2022 in Aizu, Japan. The 14 full papers included in this volume were carefully reviewed and selected from 70 submissions. They were organized in topical sections as follows: big data analytics, networking, social media, search, information extraction, image processing and analysis, spatial, text, mobile and graph data analysis, machine learning, and healthcare.
9783031283505
10.1007/978-3-031-28350-5 doi
Big data.
Big Data.
QA76.9.B45
005.7
Data Science: Systems -- Architectures -- Big Data Analytics in Healthcare Support Systems -- Information Interchange of Web Data Resources -- Business Analytics.
This book constitutes the proceedings of the 10th International Conference on Big Data Analytics, BDA 2022, which took place in a hybrid mode during December 2022 in Aizu, Japan. The 14 full papers included in this volume were carefully reviewed and selected from 70 submissions. They were organized in topical sections as follows: big data analytics, networking, social media, search, information extraction, image processing and analysis, spatial, text, mobile and graph data analysis, machine learning, and healthcare.
9783031283505
10.1007/978-3-031-28350-5 doi
Big data.
Big Data.
QA76.9.B45
005.7