tindResources

This repository links all codebases and artifacts relevant to the paper submission "Efficient Discovery of Temporal Inclusion Dependencies".

Code:

• Implementation of all Algorithms (scala): https://github.com/leonbornemann/temporalINDDiscovery
• Creation of Plots (jupyter notebook): https://github.com/leonbornemann/temporalINDEvaluation

Input Data:

• 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

Evaluation Data:

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.