An implementation of the metrics [email protected] and [email protected] to measure the diefficiency (or continuous efficiency) of incremental approaches, see Acosta, M., Vidal, M. E., & Sure-Vetter, Y. (2017)
R package for computing diefficiency metrics [email protected] and [email protected]
The metrics [email protected] and [email protected] allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. [email protected] and [email protected] rely on the computation of the area under the curve of answer traces, and thus capturing the answer rate concentration over a time interval.
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")
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 [email protected] when t is the time where the fastest approach produced the last answer.dieft(traces, "Q9.sparql") # Compute [email protected] after 7.5 time units (seconds) of execution. dieft(traces, "Q9.sparql", 7.5)
Learn step by step to use the dief
R package with Jupyter Notebooks.
dief
package and reproducibility of the experimental results reported at [1]: https://github.com/maribelacosta/dief-notebooks/blob/master/Dief-Intro.ipynbCheck the dief-app
Shiny app.
dief-app
at: http://km.aifb.kit.edu/services/dief-app/This package is licensed under the MIT License.
If you are using the dief
package to compute [email protected] or [email protected], 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 [email protected] or [email protected], please cite our main publication [1].
[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 [email protected] and [email protected] In the International Semantic Web Conference, Posters and Demos, 2017.