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Mining spatiotemporal co-occurrence patterns in non-relational databases

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Abstract

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining.

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Notes

  1. The Apache Accumulo - https://accumulo.apache.org/

  2. HBase– http://hbase.apache.org/

  3. Cassandra– ‘http://cassandra.apache.org/

  4. http://grid.cs.gsu.edu/~baydin2/proj/nonrelstcop.html

  5. Heliophysics Event Registry - https://www.lmsal.com/hek/api.html

  6. NBA.com/Stats - http://stats.nba.com/

  7. Amazon Web Services - Cloud Computing Services – http://aws.amazon.com/

  8. JTS Topology Suite – http://www.vividsolutions.com/jts/JTSHome.htm

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Acknowledgments

This work was supported in part by two NASA Grant Awards (No. NNX11AM13A, and No. NNX15AF39G), and one NSF Grant Award (No. AC1443061). The NSF Grant Award has been supported by funding from the Division of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering, the Division of Astronomical Sciences within the Directorate for Mathematical and Physical Sciences, and the Division of Atmospheric and Geospace Sciences within the Directorate for Geosciences.

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  1. Department of Computer Science, Georgia State University, 25 Park Place, Suite 700, Atlanta, GA, 30303, USA

    Berkay Aydin, Vijay Akkineni & Rafal Angryk

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  1. Berkay Aydin
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  2. Vijay Akkineni
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  3. Rafal Angryk
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Correspondence to Berkay Aydin.

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Aydin, B., Akkineni, V. & Angryk, R. Mining spatiotemporal co-occurrence patterns in non-relational databases. Geoinformatica 20, 801–828 (2016). https://doi.org/10.1007/s10707-016-0255-0

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