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Factorization techniques for longitudinal linked data

Short Paper
 
: Karim, F.; Vidal, M.-E.; Auer, S.

:

Debruyne, C.:
On the move to meaningful Internet systems. OTM Conferences 2016 : Confederated International Conferences: CoopIS, C&TC, and ODBASE 2016, Rhodes, Greece, October 24-28, 2016; Proceedings
Cham: Springer International Publishing, 2016 (Lecture Notes in Computer Science 10033)
ISBN: 978-3-319-48471-6 (Print)
ISBN: 978-3-319-48472-3 (Online)
ISBN: 3-319-48471-0
S.690-698
OnTheMove Event (OTM) <2016, Rhodes>
International Conference on Cooperative Information Systems (CoopIS) <24, 2016, Rhodes>
International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE) <2016, Rhodes>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

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.

: http://publica.fraunhofer.de/dokumente/N-444821.html