A hybrid index for temporal big data

Abstract

Temporal index provides an important way to accelerate query performance in temporal big data. However, the current temporal index cannot support the variety of queries very well, and it is hard to take account of the efficiency of query execution as well as the index construction and maintenance. In this paper, we propose a novel segmentation-based hybrid index B+-Tree, called SHB+- tree, for temporal big data. First, the temporal data in temporal table deposited is separated to fragments according to the time order. In each segment, the hybrid index is constructed by integrating the temporal index and the object index, and the temporal big data is shared by them. The performance of construction and maintenance is improved by employing the segmented storage strategy and bottom-up index construction approaches for every part of the hybrid index. The experimental results on benchmark data set verify the effectiveness and efficiency of the proposed method.