In light of the characteristics of spatio-temporal data stream

we propose a moving object spatial index construction method called HSTRCL

which is based on time window data sorting and bulk loading. It segments the continuous spatio-temporal data stream with fixed-length time windows; after finishing caching the data of a time window

by combining parallel processing and optimized bulk loading technology

we isolate as much as possible the time-consuming work of data partitioning and sorting operations from the traditional build process

and parallize them with the reception of data streams and other build operations. Furthermore

we avoid unnecessary locking synchronization overhead. And all these techniques improve the efficiency of index construction. In addition

to meet the performance and diverse query requirements

we also adopt the primary-auxiliary index construction technology based on Hash and STR. To further improve the performance in the object query scenario

we invent another moving object spatial index construction method OAHSTRCL via time window object aggregation and bulk loading

where objects are divided more finely

and the object query time required is about 65% of HSTRCL

though it will affect the performance of spatial query to some extent. Theoretical analysis and experiments have demonstrated the effectiveness of our proposed methods.