  Predictive business process monitoring refers to the act of making
predictions about the future state of ongoing cases of a business process,
based on their incomplete execution traces and logs of historical (completed)
traces. Motivated by the increasingly pervasive availability of fine-grained
event data about business process executions, the problem of predictive process
monitoring has received substantial attention in the past years. In particular,
a considerable number of methods have been put forward to address the problem
of outcome-oriented predictive process monitoring, which refers to classifying
each ongoing case of a process according to a given set of possible categorical
outcomes - e.g., Will the customer complain or not? Will an order be delivered,
canceled or withdrawn? Unfortunately, different authors have used different
datasets, experimental settings, evaluation measures and baselines to assess
their proposals, resulting in poor comparability and an unclear picture of the
relative merits and applicability of different methods. To address this gap,
this article presents a systematic review and taxonomy of outcome-oriented
predictive process monitoring methods, and a comparative experimental
evaluation of eleven representative methods using a benchmark covering 24
predictive process monitoring tasks based on nine real-life event logs.
