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Erschienen in:
2014 | OriginalPaper | Buchkapitel
The CLOCK Data-Aware Eviction Approach: Towards Processing Linked Data Streams with Limited Resources
verfasst von : Shen Gao, Thomas Scharrenbach, Abraham Bernstein
Erschienen in: The Semantic Web: Trends and Challenges
Verlag: Springer International Publishing
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Processing streams rather than static files of Linked Data has gained increasing importance in the web of data. When processing datastreams system builders are faced with the conundrum of guaranteeing a constant maximum response time with limited resources and, possibly, no prior information on the data arrival frequency. One approach to address this issue is to delete data from a cache during processing - a process we call
eviction
. The goal of this paper is to show that datadriven eviction outperforms today’s dominant data-agnostic approaches such as first-in-first-out or random deletion.
Specifically, we first introduce a method called
Clock
that evicts data from a join cache based on the likelihood estimate of contributing to a join in the future. Second, using the well-established SR-Bench benchmark as well as a data set from the IPTV domain, we show that
Clock
outperforms data-agnostic approaches indicating its usefulness for resource-limited linked data stream processing.