Parallel SECONDO: A practical system for large-scale processing of moving objects
ICDE(2014)
摘要
Parallel Secondo scales up the capability of processing extensible data models in Secondo. It combines Hadoop with a set of Secondo databases, providing almost all existing SECONDO data types and operators. Therefore it is possible for the user to convert large-scale sequential queries to parallel queries without learning the Map/Reduce programming details. This paper demonstrates such a procedure. It imports the data from the project OpenStreetMap into Secondo databases to build up the urban traffic network and then processes network-based queries like map-matching and symbolic trajectory pattern matching. All involved queries were stated as sequential expressions and time-consuming in single-computer Secondo. However, they can achieve an impressive performance in Parallel Secondo after being converted to the corresponding parallel queries, even on a small cluster consisting of six low-end computers.
更多查看译文
关键词
secondo data types,network-based queries,parallel secondo system,parallel programming,urban traffic network,secondo databases,openstreetmap project,symbolic trajectory pattern matching,single-computer secondo,parallel queries,data models,secondo operators,moving objects processing,mapreduce programming details,sequential queries,map-matching,parallel databases,hadoop,query processing,trajectory,distributed databases,decision support systems
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络