A distributed storage and index method of trajectory big data based on the Hilbert curve
In response to the rapid growth of trajectory big data with spatio-temporal characteristics and the urgent need for its fast query, traditional relational databases have certain limitations on the storage of massive trajectory data and specific query requirements, while non-relational databases are difficult to meet the efficient indexing requirements of massive data, and the efficiency of the storage and indexing of trajectory data is still in urgent need of improvement. In this paper, a framework for storage and retrieval based on HBase database is designed and implemented to cope with the efficient management of spatio-temporal trajectory data. Firstly, a novel Rowkey structure is designed, and the GeoMesa-HBase underlying storage model is constructed by combining spatio-temporal indexing tools. Secondly, a Hilbert curve-based coding technique is integrated to construct the spatial index, which improves the storage and retrieval efficiency of trajectory data. In order to evaluate the effectiveness of the proposed method, this paper compares its storage and query performance with traditional storage databases ( HBase and MySQL) and Geohash index. The experimental results show that the scheme is able to achieve effective storage of trajectory data and improve the retrieval efficiency, which is of great practical significance in addressing the challenges associated with trajectory big data management.
万方数据
维普
