A Novel Query Method for Spatial Data in Mobile Cloud Computing Environment

Abstract

With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly.

A Survey of Big Data Analytics for Smart Forestry

“…The data loaded into memory is stored in the cluster in the form of splits. When the data in the memory needs to be further refined, the system can prune the splits using a specific strategy according to the spatial relationship of the query object, such as the thin-MBR and fat-MBR strategies [64], [65]. The method used for range query is that after the client submits the request, the system accesses the local index in parallel in each partition and quickly finds the data that meets the requirements in each partition.…”