TY - GEN
T1 - A demonstration of GeoSpark
T2 - 32nd IEEE International Conference on Data Engineering, ICDE 2016
AU - Yu, Jia
AU - Wu, Jinxuan
AU - Elsayed, Mohamed
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - This paper demonstrates GEOSPARK a cluster computing framework for developing and processing large-scale spatial data analytics programs. GEOSPARK consists of three main layers: Apache Spark Layer, Spatial RDD Layer and Spatial Query Processing Layer. Apache Spark Layer provides basic Apache Spark functionalities as regular RDD operations. Spatial RDD Layer consists of three novel Spatial Resilient Distributed Datasets (SRDDs) which extend regular Apache Spark RDD to support geometrical and spatial objects with data partitioning and indexing. Spatial Query Processing Layer executes spatial queries (e.g., Spatial Join) on SRDDs. The dynamic status of SRDDs and spatial operations are visualized by GEOSPARK monitoring map interface. We demonstrate GEOSPARK using three spatial analytics applications (spatial aggregation, autocorrelation and co-location) to show how users can easily define their spatial analytics tasks and efficiently process such tasks on large-scale spatial data at interactive performance.
AB - This paper demonstrates GEOSPARK a cluster computing framework for developing and processing large-scale spatial data analytics programs. GEOSPARK consists of three main layers: Apache Spark Layer, Spatial RDD Layer and Spatial Query Processing Layer. Apache Spark Layer provides basic Apache Spark functionalities as regular RDD operations. Spatial RDD Layer consists of three novel Spatial Resilient Distributed Datasets (SRDDs) which extend regular Apache Spark RDD to support geometrical and spatial objects with data partitioning and indexing. Spatial Query Processing Layer executes spatial queries (e.g., Spatial Join) on SRDDs. The dynamic status of SRDDs and spatial operations are visualized by GEOSPARK monitoring map interface. We demonstrate GEOSPARK using three spatial analytics applications (spatial aggregation, autocorrelation and co-location) to show how users can easily define their spatial analytics tasks and efficiently process such tasks on large-scale spatial data at interactive performance.
UR - https://www.scopus.com/pages/publications/84980328112
UR - https://www.scopus.com/pages/publications/84980328112#tab=citedBy
U2 - 10.1109/ICDE.2016.7498357
DO - 10.1109/ICDE.2016.7498357
M3 - Conference contribution
AN - SCOPUS:84980328112
T3 - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
SP - 1410
EP - 1413
BT - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 May 2016 through 20 May 2016
ER -