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ACM Computing Surveys (CSUR)
Data Management in Location-Dependent Information Services
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Revisiting R-Tree Construction Principles
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
Supporting Complex Multi-Dimensional Queries in P2P Systems
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Using a distributed quadtree index in peer-to-peer networks
The VLDB Journal — The International Journal on Very Large Data Bases
MapReduce: simplified data processing on large clusters
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ACM Transactions on Computer Systems (TOCS)
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ACM SIGOPS Operating Systems Review
The Hadoop Distributed File System
MSST '10 Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST)
Advanced Geoinformation Science
Advanced Geoinformation Science
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Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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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.