Running Experiments with Confidence and Sanity

Martin Aumüller, Matteo Ceccarello

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

Analyzing data from large experimental suites is a daily task for anyone doing experimental algorithmics. In this paper we report on several approaches we tried for this seemingly mundane task in a similarity search setting, reflecting on the challenges it poses.

We conclude by proposing a workflow, which can be implemented using several tools, that allows to analyze experimental data with confidence.

The extended version of this paper and the support code are provided at https://github.com/Cecca/running-experiments.
Original languageEnglish
Title of host publicationInternational Conference on Similarity Search and Applications
PublisherSpringer
Publication date2020
Pages18-32
DOIs
Publication statusPublished - 2020

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