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The OTree: multidimensional indexing with efficient data sampling for HPC

Author
Cugnasco, C.; Calmet, H.; Santamaria, P.; Sirvent, R.; Eguzkitza, A. B.; Houzeaux, G.; Becerra, Y.; Torres, J.; Labarta, J.
Type of activity
Presentation of work at congresses
Name of edition
IEEE International Conference on Big Data 2019
Date of publication
2019
Presentation's date
2019-12
Book of congress proceedings
2019 IEEE International Conference on Big Data: Dec 9-Dec 12, 2019, Los Angeles, CA, USA: proceedings
First page
433
Last page
440
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
10.1109/BigData47090.2019.9006121
Repository
http://hdl.handle.net/2117/180358 Open in new window
URL
https://ieeexplore.ieee.org/document/9006121 Open in new window
Abstract
Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O ...
Citation
Cugnasco, C. [et al.]. The OTree: multidimensional indexing with efficient data sampling for HPC. A: IEEE International Conference on Big Data. "2019 IEEE International Conference on Big Data: Dec 9-Dec 12, 2019, Los Angeles, CA, USA: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 433-440.
Keywords
Distributed data store, High-performance computing, Multidimensional indexing
Group of research
CAP - High Performace Computing Group

Participants

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