ST-HBase: A Scalable Data Management System for Massive Geo-tagged Objects

Abstract

In this paper, we propose ST-HBase (spatio-textual HBase) that can deal with large scale geo-tagged objects. ST-HBase can support high insert throughput while providing efficient spatial keyword queries. To the best of our knowledge, the existing approaches that deal with spatial keyword queries mainly focus on the static and medium-sized objects collections and cannot provide high insert throughput. In ST-HBase, we leverage an index module layered over a key-value store. The underlying key-value store enables the system to sustain high insert throughput and deal with large scale data, the index layer can provide efficient spatial keyword query processing. We propose two kinds of index approaches in ST-HBase: Spatial and Textual Based Hybrid Index(STbHI) and Term Cluster Based Inverted Spatial Index(TCbISI) which are suitable for different scenarios. We implement a prototype based on HBase that is a standard open-source key-value store. Finally we perform comprehensive experiments and the results show that ST-HBase has good scalability and outperforms the state-of-the-art approaches in terms of update and query performance.