For various entering and transmission issues raised by human or system, missing events often occur in event data, which record execution logs of business processes. Without recovering the missing events, applications such as provenance analysis or complex event processing built upon event data are not reliable. Following the minimum change discipline in improving data quality, it is also rational to find a recovery that minimally differs from the original data. Existing recovery approaches fall short of efficiency owing to enumerating and searching over all of the possible sequences of events. In this paper, we study the efficient techniques for recovering missing events. According to our theoretical results, the recovery problem appears to be NP-hard. Nevertheless, advanced indexing, pruning techniques are developed to further improve the recovery efficiency. The experimental results demonstrate that our minimum recovery approach achieves high accuracy, and significantly outperforms the state-of-the-art technique for up to five orders of magnitudes improvement in time performance.
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