Integration and massive storage of hydro-meteorological data combining big data & semantic web technologies

Autores/as

  • Andrés Tello Universidad de Cuenca
  • Renán Freire
  • Mauricio Espinoza
  • Víctor Saquicela

Resumen

ABSTRACT
Ecuador contains an immense collection of hydro-meteorological data, informing us via standards how to locate and invoke them. If we want to make such data easier to understand and use, we need to store them in a common repository and annotate them by means of descriptive metadata. This paper proposes an approach for the massive storage, integration and semantic annotation of hydro-meteorological data using an open source integration container, NoSQL databases and Semantic Web technologies. The main contributions of this paper are: i) a shared common repository of hydro-meteorological data, ii) automatic semantic annotation to formally describe the data sources, and iii) efficient mechanisms for searching and retrieving hydro meteorological data.
Keywords: Hydro-meteorological data, data integration, big data, semantic web, NoSQL.

RESUMEN
Ecuador contiene una inmensa colección de datos hidro-meteorológicos usualmente descritos usando estándares que nos indican cómo localizarlos y cómo invocarlos. Si queremos hacer que esos datos sean potencialmente más sencillos de entender y usar se requiere almacenarlos en un repositorio común y anotarlos formalmente usando metadatos descriptivos. Este artículo propone un mecanismo para el almacenamiento masivo, integración y anotación semántica de datos hidro-meteorológicos utilizando un framework de integración, bases de datos NoSQL y tecnologías de web semántica. Las contribuciones principales de este artículo son: i) Un repositorio compartido de datos hidro-meteorológicos, ii) anotación semántica automática para describir las fuentes de manera formal, y iii) mecanismos eficientes de búsqueda y consulta de datos hidro-meteorológicos
Palabras clave: Datos hidro-meteorológicos, integración de datos, big data, web semántica, NoSQL.

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Citas

Aberer, K., M. Hauswirth, A. Salehi, 2006. The Global Sensor Networks middleware for efficient and flexible deployment and interconnection of sensor networks. ACM/IFIP/USENIX 7th International Middleware Conference, EPFL, Lausanne, Switzerland.

Corcho, Ó., D. Garijo Verdejo, J. Mora, M. Poveda Villalon, D. Vila Suero, B. Villazón-Terrazas, G.A. Atemezing, 2012. Transforming meteorological data into linked data. Undefined, 1, 1-5, IOS Press. Available at http://www.semantic-web-journal.net/sites/default/files/swj281_0.pdf.

Berners-Lee, T., J. Hendler, O. Lassila, 2001. The semantic web. Scientific American, 284(5), 28-37.

Bifet, A., 2013. Mining big data in real time. Informatica, 37(1).

Borthakur, D., 2007. The hadoop distributed file system: Architecture and design. Hadoop Project Website, 11, 21

Cudré-Mauroux, P., I. Enchev, S. Fundatureanu, P. Groth, A. Haque, A. Harth, F.L. Keppmann, D. Miranker, J.F. Sequeda, M. Wylot, 2013. Nosql databases for rdf: an empirical evaluation. In: The Semantic Web-ISWC conference, 310-325.

Cuesta, C.E., M.A. Mart ınez-Prieto, J.D. Fernández, 2013. Towards an architecture for managing big semantic data in real-time. Software Architecture, 45-53.

Fung, D.S.C., 2006. Methods for the estimation of missing values in time series. PhD thesis, Edith Cowan University, Perth, Australia.

Patni, H., C. Henson, A. Sheth, 2010. Linked sensor data. In: Collaborative Technologies and Systems (CTS). IEEE International Symposium, 362-370.

Patni, H., C.A. Henson, M. Cooney, A.P. Sheth, K. Thirunarayan, 2011. Demonstration: real-time semantic analysis of sensor streams. Available at http://corescholar.libraries.wright.edu/ cgi/viewcontent.cgi?article=1249&context=knoesis.
RDF Working Group, 2014. Resource Description Framework (RDF). Available at https://www.w3. org/2001/sw/wiki/RDF.

Sagiroglu, S., D. Sinanc, 2013. Big data: A review. In: Collaboration Technologies and Systems (CTS), IEEE International Conference, 42-47.

Shvachko, K., H. Kuang, S. Radia, R. Chansler, 2010. The hadoop distributed file system. In: Mass Storage Systems and Technologies (MSST), IEEE 26th Symposium, 1-10.

Villars, R.L., C.W. Olofson, M. Eastwood, 2011. Big data: What it is and why you should care. White Paper, IDC. Available at http://www.admin-magazine.com/HPC/Vendors/AMD/Whitepaper-Big-Data-What-It-Is-and-Why-You-Should-Care.

Zeng, K., J. Yang, H. Wang, B. Shao, Z. Wang, 2013. A distributed graph engine for web scale rdf data. Proceedings of the VLDB Endowment, 6(4), 265-276.

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Publicado

2015-12-05

Cómo citar

Tello, A., Freire, R., Espinoza, M., & Saquicela, V. (2015). Integration and massive storage of hydro-meteorological data combining big data & semantic web technologies. Maskana, 6(Supl.), 165–172. Recuperado a partir de https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/711

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