Provenance comparison for large-scale knowledge discovery

Provenance is a record that describes entities and processes involved in producing, delivering and influencing a resource. Provenance management and reuse can enable interesting applications for knowledge discovery and analytics. One crucial component of a provenance management system is the comparison between provenances. In the era of big data, provenance management systems are in need of a scalable algorithmic solution for efficient comparison. Existing solutions to the problem have large memory footprint and require overlong system response time. In this paper, we present a new solution to threshold-based provenance comparison. We model provenance directly as graph, and propose to measure provenance similarity using provenance edit distance. Following the depth-first search paradigm, we design an algorithm PEDSim based on an encoding technique specific to provenance graphs and quantifiable heuristics. Extensive experiments on real data demonstrate the superiority of our method to other alternatives.