Elastic and effective spatio-temporal query processing scheme on Hadoop

Geospatial applications have become prevalent in both scientific research and industry. Spatio-Temporal query processing is a fundamental issue for driving geospatial applications. However, the state-of-the-art spatio-temporal query processing methods are facing significant challenges as the data expand and concurrent users increase. In this paper we present a novel spatio-temporal querying scheme to provide efficient query processing over big geospatial data. The scheme improves query efficiency from three facets. Firstly, taking geographic proximity and storage locality into consideration, we propose a geospatial data organization approach to achieve high aggregate I/O throughput, and design a distributed indexing framework for efficient pruning of the search space. Furthermore, we design an indexing plus MapReduce query processing architecture to improve data retrieval efficiency and query computation efficiency. In addition, we design distributed caching model to accelerate the access response of hotspot spatial objects. We evaluate the effectiveness of our scheme with comprehensive experiments using real datasets and application scenarios.