Abstract
Big Data Management has become a critical task in many application systems, which usually rely on heavyweight batch processes to process large amounts of data. However, batch architectures are not an adequate choice for the design of real-time systems, where expected response times are several orders of magnitude underneath. This paper outlines the foundations for defining an architecture able to deal with such an scenario, fulfilling the specific needs of real-time systems which expose big RDF datasets. Our proposal (Solid) is a tiered architecture which separates the complexities of Big Data management from their real-time data generation and consumption. Big semantic data are stored and indexed in a compressed way following the Rdf/Hdt proposal; while at the same time, real-time requirements are addressed using NoSQL technology. Both are efficient layers, but their approaches are quite different and their combination is not easy. Two additional layers are required to achieve an overall high performance, satisfying real-time needs, and able to work even in a mobile context.
This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through Projects TIN2012-31104, TIN2009-13838 and TIN2009-14009-C02-0; and also by Chilean Fondecyt Grant 1-110066, the Regional Government of Castilla y Leon and the ESF.
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References
Abadi, D., Marcus, A., Madden, S., Hollenbach, K.: Scalable semantic Web data management using vertical partitioning. In: Proc. of VLDB, pp. 411–422 (2007)
Beckett, D. (ed.): RDF/XML Syntax Specification. W3C Recommendation (2004)
Begoli, E., Horey, J.: Design Principles for Effective Knowledge Discovery from Big Data. In: Proc. 2012 Joint WICSA/ECSA Conference, pp. 215–218. IEEE (August 2012)
Berners-Lee, T.: Linked Data: Design Issues (2006), http://www.w3.org/DesignIssues/LinkedData.html (retrieved on March 01, 2013)
Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (2001)
De, S., Elsaleh, T., Barnaghi, P., Meissner, S.: An Internet of Things Platform for Real-World and Digital Objects. Scalable Computing: Practice and Experience 13(1) (2012)
Fernández, J., Martínez-Prieto, M., Gutiérrez, C., Polleres, A., Arias, M.: Binary RDF representation for publication and exchange (HDT). Journal of Web Semantics (in press, 2013), http://dx.doi.org/10.1016/j.websem.2013.01.002
Genovese, Y., Prentice, S.: Pattern-Based Strategy: Getting Value from Big Data. Gartner Special Report (June 2011)
Halfon, A.: Handling Big Data Variety, http://www.finextra.com/community/fullblog.aspx?blogid=6129 (retrieved on March 01, 2013)
Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space. Morgan & Claypool (2011)
Loukides, M.: Data Science and Data Tools. In: Big Data Now, ch. 1. O’Reilly (2012)
Manola, F., Miller, E. (eds.): RDF Primer. W3C Recommendation (2004)
Martínez-Prieto, M.A., Arias Gallego, M., Fernández, J.D.: Exchange and Consumption of Huge RDF Data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 437–452. Springer, Heidelberg (2012)
Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning (2013)
Prud’hommeaux, E., Seaborne, A. (eds.): SPARQL Query Language for RDF. W3C Recommendation (2008), http://www.w3.org/TR/rdf-sparql-query/
Styles, R.: RDF, Big Data and The Semantic Web, http://dynamicorange.com/2012/04/24/rdf-big-data-and-the-semantic-web/ (retrieved on March 01, 2013)
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Cuesta, C.E., Martínez-Prieto, M.A., Fernández, J.D. (2013). Towards an Architecture for Managing Big Semantic Data in Real-Time. In: Drira, K. (eds) Software Architecture. ECSA 2013. Lecture Notes in Computer Science, vol 7957. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39031-9_5
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DOI: https://doi.org/10.1007/978-3-642-39031-9_5
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