Parallel SECONDO: A practical system for large-scale processing of moving objects

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.