Consistent process mining over big data triple stores

'Big Data' techniques are often adopted in cross-organization scenarios for integrating multiple data sources to extract statistics or other latent information. Even if these techniques do not require the support of a schema for processing data, a common conceptual model is typically defined to address name resolution. This implies that each local source is tasked of applying a semantic lifting procedure for expressing the local data in term of the common model. Semantic heterogeneity is then potentially introduced in data. In this paper we illustrate a methodology designed to the implementation of consistent process mining algorithms in a 'Big Data' context. In particular, we exploit two different procedures. The first one is aimed at computing the mismatch among the data sources to be integrated. The second uses mismatch values to extend data to be processed with a traditional map reduce algorithm.