Abstract
Longitudinal linked data are RDF descriptions of observations from related sampling frames or sensors at multiple points in time, e.g., patient medical records or climate sensor data. Observations are expressed as measurements whose values can be repeated several times in a sampling frame, resulting in a considerable increase in data volume. We devise a factorized compact representation of longitudinal linked data to reduce repetition of same measurements, and propose algorithms to generate collections of factorized longitudinal linked data that can be managed by existing RDF triple stores. We empirically study the effectiveness of the proposed factorized representation on linked observation data. We show that the total data volume can be reduced by more than 30 % on average without loss of information, as well as improve compression ratio of state-of-the-art compression techniques.
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Notes
- 1.
Prefixes are used as in https://www.w3.org/wiki/SRBench; additionally, we consider the prefix: ld:\(\mathtt{<}\) http://longitudinallinkeddata.com/ontology#\(\mathtt{>}\).
- 2.
Available at: http://wiki.knoesis.org/index.php/LinkedSensorData.
- 3.
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Acknowledgements
This work is supported in part by the EU H2020 programme for the project BigDataEurope (GA 644564). Farah Karim is supported by a scholarship of German Academic Exchange Service (DAAD).
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Karim, F., Vidal, ME., Auer, S. (2016). Factorization Techniques for Longitudinal Linked Data (Short Paper). In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_42
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DOI: https://doi.org/10.1007/978-3-319-48472-3_42
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