Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27992
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Dividino, Renata
Gottron, Thomas
Scherp, Ansgar
Contact Email: ansgar.scherp@stir.ac.uk
Title: Strategies for efficiently keeping local linked open data caches up-to-date
Editor(s): Simperl, Elena
Arenas, Marcelo
Corcho, Oscar
Strohmaier, Markus
d'Aquin, Mathieu
Srinivas, Kavitha
Groth, Paul
Dumontier, Michel
Heflin, Jeff
Thirunarayan, Krishnaprasad
Staab, Steffen
Citation: Dividino R, Gottron T & Scherp A (2015) Strategies for efficiently keeping local linked open data caches up-to-date. In: Simperl E, Arenas M, Corcho O, Strohmaier M, d'Aquin M, Srinivas K, Groth P, Dumontier M, Heflin J, Thirunarayan K & Staab S (eds.) The Semantic Web - ISWC 2015. ISWC 2015. Lecture Notes in Computer Science, 9367. 14th International Semantic Web Conference, ISWC 2015, Bethlehem, PA, USA, 11.10.2015-15.10.2015. Cham, Switzerland: Springer Verlag, pp. 356-373. https://doi.org/10.1007/978-3-319-25010-6_24
Issue Date: 31-Dec-2015
Series/Report no.: Lecture Notes in Computer Science, 9367
Conference Name: 14th International Semantic Web Conference, ISWC 2015
Conference Dates: 2015-10-11 - 2015-10-15
Conference Location: Bethlehem, PA, USA
Abstract: Quite often, Linked Open Data (LOD) applications pre-fetch data from the Web and store local copies of it in a cache for faster access at runtime. Yet, recent investigations have shown that data published and interlinked on the LOD cloud is subject to frequent changes. As the data in the cloud changes, local copies of the data need to be updated. However, due to limitations of the available computational resources (e.g., network bandwidth for fetching data, computation time) LOD applications may not be able to permanently visit all of the LOD sources at brief intervals in order to check for changes. These limitations imply the need to prioritize which data sources should be considered first for retrieving their data and synchronizing the local copy with the original data. In order to make best use of the resources available, it is vital to choose a good scheduling strategy to know when to fetch data of which data source. In this paper, we investigate different strategies proposed in the literature and evaluate them on a large-scale LOD dataset that is obtained from the LOD cloud by weekly crawls over the course of three years. We investigate two different setups: (i) in the single step setup, we evaluate the quality of update strategies for a single and isolated update of a local data cache, while (ii) the iterative progression setup involves measuring the quality of the local data cache when considering iterative updates over a longer period of time. Our evaluation indicates the effectiveness of each strategy for updating local copies of LOD sources, i.e, we demonstrate for given limitations of bandwidth, the strategies’ performance in terms of data accuracy and freshness. The evaluation shows that the measures capturing change behavior of LOD sources over time are most suitable for conducting updates.
Status: VoR - Version of Record
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