Awesome

dief

R package for computing diefficiency metrics dief@t and dief@k.

The metrics dief@t and dief@k allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. dief@t and dief@k rely on the computation of the area under the curve of answer traces, and thus capturing the answer rate concentration over a time interval.

Download and Install

To download the development version of the dief package directly from GitHub, type the following at the R command line:

# If you have not installed the "devtools" package.
install.packages("devtools")
# Install the dief package.
devtools::install_github("maribelacosta/dief")

Examples

library("dief")

# Use answer traces provided in the package: Compare three approaches "Selective", "Not Adaptive", "Random" when executing the test "Q9.sparql".
traces
	
# Plot answer traces for test "Q9.sparql".
plotAnswerTrace(traces, "Q9.sparql")
	
# Compute dief@t when t is the time where the slowest approach produced the last answer.
dieft(traces, "Q9.sparql")
	
# Compute dief@t after 7.5 time units (seconds) of execution. 
dieft(traces, "Q9.sparql", 7.5)

Other Resources

Learn step by step to use the dief R package with Jupyter Notebooks.

Introduction to the dief package and reproducibility of the experimental results reported at [1]: https://github.com/maribelacosta/dief-notebooks/blob/master/Dief-Intro.ipynb

Check the dief-app Shiny app.

Visualize the dief-app at: http://km.aifb.kit.edu/services/dief-app/

License

This package is licensed under the MIT License.

How to Cite

If you are using the dief package to compute dief@t or dief@k, please cite the dief package using the citation generated with the R built-in command citation("dief") as follows:

library("dief")
citation("dief")

In addition, if you are reporting dief@t or dief@k, please cite our main publication [1].

Publications

[1] Maribel Acosta, Maria-Esther Vidal, York Sure-Vetter. Diefficiency Metrics: Measuring the Continuous Efficiency of Query Processing Approaches. In Proceedings of the International Semantic Web Conference, 2017. Nominated to Best Paper Award at the Resource Track. https://doi.org/10.1007/978-3-319-68204-4_1

[2] Maribel Acosta, Maria-Esther Vidal. Measuring the Performance of Continuous Query Processing Approaches with dief@t and dief@k. In the International Semantic Web Conference, Posters and Demos, 2017.