  In this paper, we propose DeepAlign, a novel approach to multi-perspective
process anomaly correction, based on recurrent neural networks and
bidirectional beam search. At the core of the DeepAlign algorithm are two
recurrent neural networks trained to predict the next event. One is reading
sequences of process executions from left to right, while the other is reading
the sequences from right to left. By combining the predictive capabilities of
both neural networks, we show that it is possible to calculate sequence
alignments, which are used to detect and correct anomalies. DeepAlign utilizes
the case-level and event-level attributes to closely model the decisions within
a process. We evaluate the performance of our approach on an elaborate data
corpus of 252 realistic synthetic event logs and compare it to three
state-of-the-art conformance checking methods. DeepAlign produces better
corrections than the rest of the field reaching an overall $F_1$ score of
$0.9572$ across all datasets, whereas the best comparable state-of-the-art
method reaches $0.6411$.
