Practical Algorithmic Techniques for Several String Processing Problems

TL;DR

This paper introduces practical algorithmic techniques leveraging classical string algorithms and data structures to efficiently solve multiple string processing problems relevant in data mining and knowledge discovery.

Contribution

It presents novel, practical algorithms for common string processing problems using efficient data structures like suffix arrays and automata.

Findings

Enhanced efficiency in string pattern matching tasks

Effective solutions for maximum weight ordered common subset problem

Improved processing of large textual datasets

Abstract

The domains of data mining and knowledge discovery make use of large amounts of textual data, which need to be handled efficiently. Specific problems, like finding the maximum weight ordered common subset of a set of ordered sets or searching for specific patterns within texts, occur frequently in this context. In this paper we present several novel and practical algorithmic techniques for processing textual data (strings) in order to efficiently solve multiple problems. Our techniques make use of efficient string algorithms and data structures, like KMP, suffix arrays, tries and deterministic finite automata.