Research And Implementation Of Efficient Distributed Spatial Range Query Technology

With the advent of big data era,information data grows rapidly.The amount of spatial data is getting bigger and bigger which includes raster spatial data obtained by satellite remote sensing technology and spatial data with relation attributes such as scenic and hotel data sets.The query processing technology for this two kinds of data can effectively analyze the environmental changes and facilitate people's lives.However,the existing system fails to process the spatial data efficiently.Therefore,the processing technology of big spatial data is of great research significance.Based on the existing spatial query research,combined with the actual application scenarios and requirements,this paper focuses on the query processing technology of big spatial data.The main contributions are as follows:(1)The raster data provides elementary data such as weather and temperature in geographic research while the traditional spatial query algorithm is not efficient in processing such data.This paper proposes an efficient spatial query index and query processing algorithm for raster data.A hybrid spatial distributed index composed of quadtree,R-tree and Hash structure is constructed to realize high-efficient indexing structure about different regions.A cooperative pruning strategy based on quadtree,R-tree and Hash structure is proposed.The experimental results show that the hybrid spatial index proposed in this paper improves the efficiency of raster data query processing.(2)There is a large number of spatial object data with relational attributes on Internet such as scenic datasets with spatial location and business hours.The existing spatial keywords query processing technology does not consider relational attributes as filtering condition and take advantage of parallel processing.In order to solve the above problems,this paper maps relational attributes and spatial attributes into text,and uses distributed inverted text index to perform parallel indexing on the converted text.The query request is also converted into multiple text keywords in the mapping space to search the query result efficiently.The experiment shows that the performance of our algorithm is elevated by 20% to 30% compared with the existing algorithm in terms of indexing time and query time.