Extending Shinohara's Algorithm for Computing Descriptive (Angluin-Style) Patterns to Subsequence Patterns

TL;DR

This paper surveys recent extensions of classical Angluin-style pattern inference algorithms, particularly Shinohara's algorithm, to subsequence patterns with wildcards and gap constraints, relevant for complex event recognition in databases.

Contribution

It reviews recent work that extends traditional pattern inference methods to subsequence patterns with wildcards and gap constraints for modern database applications.

Findings

Extended pattern concepts to subsequence patterns with wildcards

Applied to complex event recognition in databases

Survey of recent algorithmic developments

Abstract

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.