Big data storage technologies: a case study for web-based LiDAR visualization

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http://hdl.handle.net/2183/40675Collections
- Investigación (FIC) [1615]
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Big data storage technologies: a case study for web-based LiDAR visualizationDate
2018Citation
D. Deibe, M. Amor and R. Doallo, "Big data storage technologies: a case study for web-based LiDAR visualization," 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 3831-3840, doi: 10.1109/BigData.2018.8622589.
Abstract
[Abstract]: Big data technologies have been growing up quickly during past years. New storage and computing solutions appear while those already established in the market are improved with new features and better performance. Along with this growth also rises the number of applications and fields where the inclusion of big data technologies provides a large number of benefits, from the reduction in computational costs and economic resources to the improvement in the quality of the services provided which has a direct impact on the customers satisfaction. LiDAR (Light Detection and Ranging) data processing is one of the topics that could benefit from the adoption of these kind of technologies due to the massive datasets that are being gathered nowadays, with applications in archaeology, geography, geology or forestry, among many others. An efficient management of this volume of data becomes a key point especially in visualization, computing and analytic processes. In this paper, we analyse how web applications for the visualization of LiDAR data can benefit from the adoption of big data storage technologies, as well as the advantages and disadvantages that may determine the choice of one of them.
Keywords
Big Data
Data visualization
Laser radar
LiDAR
storage technologies
web applications
Three-dimensional displays
Metadata
Data visualization
Laser radar
LiDAR
storage technologies
web applications
Three-dimensional displays
Metadata
Description
Presented at: 2018 IEEE International Conference on Big Data (Big Data), Seattle, 10 December 2018 through 13 December This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of
Record is available online at: https://doi.org/10.1109/BigData.2018.8622589
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© 2018 IEEE.
ISBN
978153865035-6