An Evaluation of Models for Runtime Approximation in Link Discovery

TL;DR

This paper investigates non-linear models, specifically exponential and mixed models, for estimating runtime in link discovery, demonstrating their potential benefits over linear models in certain scenarios.

Contribution

It introduces and evaluates non-linear runtime estimation models for link discovery, expanding beyond the previously used linear models.

Findings

Exponential and mixed models fit runtime data better in some cases.

Better runtime models improve overall link discovery execution.

Linear models are not always optimal for runtime estimation.

Abstract

Time-efficient link discovery is of central importance to implement the vision of the Semantic Web. Some of the most rapid Link Discovery approaches rely internally on planning to execute link specifications. In newer works, linear models have been used to estimate the runtime the fastest planners. However, no other category of models has been studied for this purpose so far. In this paper, we study non-linear runtime estimation functions for runtime estimation. In particular, we study exponential and mixed models for the estimation of the runtimes of planners. To this end, we evaluate three different models for runtime on six datasets using 400 link specifications. We show that exponential and mixed models achieve better fits when trained but are only to be preferred in some cases. Our evaluation also shows that the use of better runtime approximation models has a positive impact on the overall execution of link specifications.