The advent of Hadoop has inspired many researchers to conduct studies on big data. These studies have covered a wide range of aspects of big spatial data. However, they still face challenges in visualizing big spatial data on a distributed storage model since loading multi-resolution data is inefficient. For this reason, multi-resolution data are usually excluded from the distributed storage model to speed up the loading process. This limitation prompted the introduction of B-EagleV, a novel Hadoop-based solution that enables users to manage and visualize massive point cloud data on Hadoop Distributed File System (HDFS) without moving the multi-resolution data to a local server. This paper presents the achievements of B-EagleV in efforts to discover the values of Hadoop in visualizing massive point cloud data.
|Title of host publication||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Editors||Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|Publication status||Published - 2019 Dec|
|Event||2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States|
Duration: 2019 Dec 9 → 2019 Dec 12
|Name||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Conference||2019 IEEE International Conference on Big Data, Big Data 2019|
|Period||19/12/9 → 19/12/12|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2018R1A2B2009160).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management