TY - GEN
T1 - Geospatial data management in apache spark
T2 - 35th IEEE International Conference on Data Engineering, ICDE 2019
AU - Yu, Jia
AU - Sarwat, Mohamed
N1 - Funding Information:
Mohamed Sarwat is an Assistant Professor of Computer Science and the director of the Data Systems lab at Arizona State University. Before joining ASU, Mohamed obtained his PhD degree in computer science from University of Minnesota in 2014. His research interest lies in the broad area of data management systems. Mohamed is a recipient of the University of Minnesota doctoral dissertation fellowship.His research work has been recognized by two best research paper awards in MDM 2015 and SSTD 2011 as well as a Best of Conference citation in ICDE 2012. He also received CCC Blue Sky Ideas award for best vision papers (3rd place) in SSTD 2017. Mohamed is an associate editor for the GeoInformatica journal and has served as a PC member for major data management and spatial computing venues.
Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/4
Y1 - 2019/4
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
UR - http://www.scopus.com/inward/record.url?scp=85067923448&partnerID=8YFLogxK
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
Y2 - 8 April 2019 through 11 April 2019
ER -