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With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. This paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the \(\mathcal{HR}^3\) approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery.

When to Reach for the Cloud: Using Parallel Hardware for Link Discovery