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Author: Daniel Tovarňák

Affiliation: Masaryk University, Czech Republic

ISBN: 978-989-758-194-6

Keyword(s): Log Processing, Pattern Matching, Log Abstraction, Big Data.

Related Ontology Subjects/Areas/Topics: Distributed and Mobile Software Systems ; Parallel and High Performance Computing ; Software Engineering

Abstract: Log abstraction, i.e. the separation of static and dynamic part of log message, is becoming an indispensable task when processing logs generated by large enterprise systems and networks. In practice, the log message types are described via regex matching patterns that are in turn used to actually facilitate the abstraction process. Although the area of multi-regex matching is well studied, there is a lack of suitable practical implementations available for common programming languages. In this paper we present an alternative approach to multi-pattern matching for the purposes of log abstraction that is based on a trie-like data structure we refer to as regex trie. REtrie is easy to implement and the real world experiments show its scalability and good performance even for thousands of matching patterns.

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Paper citation in several formats:
Tovarňák, D. (2016). Practical Multi-pattern Matching Approach for Fast and Scalable Log Abstraction.In Proceedings of the 11th International Joint Conference on Software Technologies - Volume 1: ICSOFT-EA, (ICSOFT 2016) ISBN 978-989-758-194-6, pages 319-329. DOI: 10.5220/0006006603190329

@conference{icsoft-ea16,
author={Daniel Tovarňák.},
title={Practical Multi-pattern Matching Approach for Fast and Scalable Log Abstraction},
booktitle={Proceedings of the 11th International Joint Conference on Software Technologies - Volume 1: ICSOFT-EA, (ICSOFT 2016)},
year={2016},
pages={319-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006006603190329},
isbn={978-989-758-194-6},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Software Technologies - Volume 1: ICSOFT-EA, (ICSOFT 2016)
TI - Practical Multi-pattern Matching Approach for Fast and Scalable Log Abstraction
SN - 978-989-758-194-6
AU - Tovarňák, D.
PY - 2016
SP - 319
EP - 329
DO - 10.5220/0006006603190329

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