Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce.
The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we presentHadoop-GIS- a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing.
MathCS and BMI, Emory University.
Ablimit
A
Aji
EECS, Northwestern University.
Xiling
X
Sun
MathCS and BMI, Emory University.
Hoang
H
Vo
MathCS and BMI, Emory University.
Qioaling
Q
Liu
CSE, Ohio State University.
Rubao
R
Lee
CSE, Ohio State University.
Xiaodong
X
Zhang
MathCS and BMI, Emory University.
Joel
J
Saltz
MathCS and BMI, Emory University.
Fusheng
F
Wang
Analytical Imaging; Data Warehouse; Database; Hive; MapReduce; Scientific Data Management; Spatial Query Processing