This repository links all codebases and artifacts relevant to the paper submission "Efficient Discovery of Temporal Inclusion Dependencies".
- Implementation of all Algorithms (scala): https://github.com/leonbornemann/temporalINDDiscovery
- Creation of Plots (jupyter notebook): https://github.com/leonbornemann/temporalINDEvaluation
- Original Historical Data From Wikipedia is available here
- Original Table Histories (with matched columns) are available here
- Complete Input data for our experiments with pre-processing and filters applied is available here
- IDs of randomly chosen queries are available here
- Annotated Gold Standard of INDs (labelled as genuine or not) are available here
This data can be re-created using the executable main objects of the scala implementation, but is published here for convenience
- Statistics about the performance of the different tIND approaches on the gold standard available here. This is the necessary input for the Jupyter notebook that perofrms the genuineness-evaluation.
- Statistics about the runtime with various parameter settings avilable here. This is the necessary input for the Jupyter notebook that performs the runtime evaluation.