Temporal representation for mining scientific data provenance
We propose a representation of data provenance using logical time that reduces its feature space. This temporal representation supports clustering, classification and association rule mining. Analysis of the clustering results shows that the k k -means algorithm gives the best performance. We carry out an evaluation against a multi-gigabyte synthetic provenance dataset. We also carry out an evaluation against a real provenance dataset gathered from a satellite instrument.
temporal representation provenance data mining