Abstract
In the Linked Data field, data publishers frequently materialize linksets between two different datasets using link discovery tools. To create a linkset, such tools typically execute linkage rules that retrieve data from the underlying datasets and apply matching predicates to create the links, in an often complex process. Also, such tools do not support linkset maintenance, when the datasets are updated. A simple, but costly strategy to maintain linksets up-to-date would be to fully re-materialize them from time to time. This paper presents an alternative strategy, called incremental, for maintaining linksets, based on idea that one should re-compute only the links that involve the updated resources. The paper discusses in detail the incremental strategy, outlines an implementation and describes an experiment to compare the performance of the incremental strategy with the full re-materialization of linksets.
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Acknowledgments
This work was partly funded by CNPq under grants 153908/2015-7, 557128/2009-9, 444976/2014-0, 303332/2013-1, 442338/2014-7 and 248743/2013-9 and by FAPERJ under grants e E-26-170028/2008 and E-26/201.337/2014.
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Menendez, E.S., Casanova, M.A., Vidal, V.M.P., Nunes, B.P., Lopes, G.R., Leme, L.A.P.P. (2016). Incremental Maintenance of Materialized SPARQL-Based Linkset Views. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9828. Springer, Cham. https://doi.org/10.1007/978-3-319-44406-2_7
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DOI: https://doi.org/10.1007/978-3-319-44406-2_7
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