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SODA: A framework for spatial observation data analysis

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Abstract

Very large amounts of geospatial data are daily generated by many observation processes in different application domains. The amount of produced data is increasing due to the advances in the use of modern automatic sensing devices and also in the facilities available to promote crowdsourcing data collection initiatives. Spatial observation data includes both data of conventional entities and also samplings over multi-dimensional spaces. Existing observation data management solutions lack declarative specification of spatio-temporal analytics. On the other hand, current data management technologies miss observation data semantics and fail to integrate the management of entities and samplings in a single data modeling solution. The present paper presents the design of a framework that enables spatio-temporal declarative analysis over large warehouses of observation data. It integrates the management of entities and samplings within a simple data model based on the well known mathematical concept of function. Observation data semantics are incorporated into the model with appropriate metadata structures.

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Acknowledgments

This work has been partially supported by the Spanish Ministry of Science and Innovation (TIN2010-21246-C02-02). The authors are also grateful to the reviewers, whose comments contributed to greatly improve the paper.

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Authors and Affiliations

  1. Computer Graphics and Data Engineering Group (COGRADE), Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Sebastián Villarroya, José R. R. Viqueira, Manuel A. Regueiro, José A. Taboada & José M. Cotos

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  1. Sebastián Villarroya
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  2. José R. R. Viqueira
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  3. Manuel A. Regueiro
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Correspondence to José R. R. Viqueira.

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Villarroya, S., Viqueira, J.R.R., Regueiro, M.A. et al. SODA: A framework for spatial observation data analysis. Distrib Parallel Databases 34, 65–99 (2016). https://doi.org/10.1007/s10619-014-7165-7

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