  Data of sequential nature arise in many application domains in forms of, e.g.
textual data, DNA sequences, and software execution traces. Different research
disciplines have developed methods to learn sequence models from such datasets:
(i) in the machine learning field methods such as (hidden) Markov models and
recurrent neural networks have been developed and successfully applied to a
wide-range of tasks, (ii) in process mining process discovery techniques aim to
generate human-interpretable descriptive models, and (iii) in the grammar
inference field the focus is on finding descriptive models in the form of
formal grammars. Despite their different focuses, these fields share a common
goal - learning a model that accurately describes the behavior in the
underlying data. Those sequence models are generative, i.e, they can predict
what elements are likely to occur after a given unfinished sequence. So far,
these fields have developed mainly in isolation from each other and no
comparison exists. This paper presents an interdisciplinary experimental
evaluation that compares sequence modeling techniques on the task of
next-element prediction on four real-life sequence datasets. The results
indicate that machine learning techniques that generally have no aim at
interpretability in terms of accuracy outperform techniques from the process
mining and grammar inference fields that aim to yield interpretable models.
