  The introduction of pattern languages in the seminal work [Angluin, ``Finding
Patterns Common to a Set of Strings'', JCSS 1980] has revived the classical
model of inductive inference (learning in the limit, gold-style learning). In
[Shinohara, ``Polynomial Time Inference of Pattern Languages and Its
Application'', 7th IBM Symposium on Mathematical Foundations of Computer
Science 1982] a simple and elegant algorithm has been introduced that, based on
membership queries, computes a pattern that is descriptive for a given sample
of input strings (and, consequently, can be employed in strategies for
inductive inference). In this paper, we give a brief survey of the recent work
[Kleest-Mei{\ss}ner et al., ``Discovering Event Queries from Traces: Laying
Foundations for Subsequence-Queries with Wildcards and Gap-Size Constraints'',
ICDT 2022], where the classical concepts of Angluin-style (descriptive)
patterns and the respective Shinohara's algorithm are extended to a query class
with applications in complex event recognition -- a modern topic from
databases.
