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.
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CITATION STYLE
Aumüller, M., & Ceccarello, M. (2020). Running experiments with confidence and sanity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12440 LNCS, pp. 387–395). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60936-8_31
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