Abstract
The volume of spatial data increases at a staggering rate. This tutorial comprehensively studies how existing works extend Apache Spark to uphold massive-scale spatial data. During this 1.5 hour tutorial, we first provide a background introduction of the characteristics of spatial data and the history of distributed data management systems. A follow-up section presents the common approaches used by the practitioners to extend Spark and introduces the vital components in a generic spatial data management system. The third, fourth and fifth sections then discuss the ongoing efforts and experience in spatial-temporal data, spatial data analytics and streaming spatial data, respectively. The sixth part finally concludes this tutorial to help the audience better grasp the overall content and points out future research directions.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 |
| Publisher | IEEE Computer Society |
| Pages | 2060-2063 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538674741 |
| DOIs | |
| State | Published - Apr 1 2019 |
| Event | 35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China Duration: Apr 8 2019 → Apr 11 2019 |
Publication series
| Name | Proceedings - International Conference on Data Engineering |
|---|---|
| Volume | 2019-April |
| ISSN (Print) | 1084-4627 |
Conference
| Conference | 35th IEEE International Conference on Data Engineering, ICDE 2019 |
|---|---|
| Country | China |
| City | Macau |
| Period | 4/8/19 → 4/11/19 |
Fingerprint
Keywords
- Apache spark
- Distributed computing
- Geospatial data
ASJC Scopus subject areas
- Software
- Signal Processing
- Information Systems
Cite this
Geospatial data management in apache spark : A tutorial. / Yu, Jia; Elsayed, Mohamed.
Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. p. 2060-2063 8731372 (Proceedings - International Conference on Data Engineering; Vol. 2019-April).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Geospatial data management in apache spark
T2 - A tutorial
AU - Yu, Jia
AU - Elsayed, Mohamed
PY - 2019/4/1
Y1 - 2019/4/1
N2 - The volume of spatial data increases at a staggering rate. This tutorial comprehensively studies how existing works extend Apache Spark to uphold massive-scale spatial data. During this 1.5 hour tutorial, we first provide a background introduction of the characteristics of spatial data and the history of distributed data management systems. A follow-up section presents the common approaches used by the practitioners to extend Spark and introduces the vital components in a generic spatial data management system. The third, fourth and fifth sections then discuss the ongoing efforts and experience in spatial-temporal data, spatial data analytics and streaming spatial data, respectively. The sixth part finally concludes this tutorial to help the audience better grasp the overall content and points out future research directions.
AB - The volume of spatial data increases at a staggering rate. This tutorial comprehensively studies how existing works extend Apache Spark to uphold massive-scale spatial data. During this 1.5 hour tutorial, we first provide a background introduction of the characteristics of spatial data and the history of distributed data management systems. A follow-up section presents the common approaches used by the practitioners to extend Spark and introduces the vital components in a generic spatial data management system. The third, fourth and fifth sections then discuss the ongoing efforts and experience in spatial-temporal data, spatial data analytics and streaming spatial data, respectively. The sixth part finally concludes this tutorial to help the audience better grasp the overall content and points out future research directions.
KW - Apache spark
KW - Distributed computing
KW - Geospatial data
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UR - http://www.scopus.com/inward/citedby.url?scp=85067923448&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2019.00239
DO - 10.1109/ICDE.2019.00239
M3 - Conference contribution
AN - SCOPUS:85067923448
T3 - Proceedings - International Conference on Data Engineering
SP - 2060
EP - 2063
BT - Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PB - IEEE Computer Society
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