The CLOCK Data-Aware Eviction Approach

Processing streams rather than static les of Linked Data has gained increasing importance in the web of data. When processing data-streams 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 data-driven eviction outperforms today's dominant data-agnostic approaches such as rst-in-rst-out or random deletion. Specically, we rst 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 bench-mark as well as a data set from the IPTV domain, we show that Clock out-performs data-agnostic approaches indicating its usefulness for resource-limited linked data stream processing.