An efficient in-memory R-tree construction scheme for spatio-temporal data stream

Abstract

In this paper, we proposed an efficient R-tree construction method by bulk loading over spatial-temporal data stream. The core idea is to partition spatial-temporal data stream into time windows and construct an R-tree for each time window. In each time window, we parallelized space partitioning and data stream reception during R-tree construction; and then we adopted sorting-based bulk loading scheme to optimize R-tree construction, which avoided unnecessary synchronization overhead and accelerated the R-tree construction. In addition, to reduce the sorting cost of R-tree bulk loading, sampling-based space partitioning scheme was introduced. Theoretical analysis and experiments demonstrated the effectiveness of our proposed method.