A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid index structures for location-based web search
Proceedings of the 14th ACM international conference on Information and knowledge management
Keyword Search on Spatial Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Keyword Search in Spatial Databases: Towards Searching by Document
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient retrieval of the top-k most relevant spatial web objects
Proceedings of the VLDB Endowment
Efficient processing of top-k spatial keyword queries
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
| Hi-index | 0.00 |
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.