TOVARŇÁK, Daniel. An Algorithm for Message Type Discovery in Unstructured Log Data. Online. In Proceedings of the 14th International Conference on Software Technologies - Volume 1: ICSOFT. Prague: SciTePress, 2019, p. 665-676. ISBN 978-989-758-379-7.
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Basic information
Original name An Algorithm for Message Type Discovery in Unstructured Log Data
Authors TOVARŇÁK, Daniel (203 Czech Republic, guarantor, belonging to the institution).
Edition Prague, Proceedings of the 14th International Conference on Software Technologies - Volume 1: ICSOFT, p. 665-676, 12 pp. 2019.
Publisher SciTePress
Other information
Original language English
Type of outcome Proceedings paper
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL URL
RIV identification code RIV/00216224:14610/19:00110676
Organization Ústav výpočetní techniky – Repository – Repository
ISBN 978-989-758-379-7
ISSN 2184-2833
Keywords in English log abstraction; message type discovery; log management; logging; unstructured data
Links EF16_019/0000822, research and development project.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 9/9/2020 05:52.
Abstract
Log message abstraction is a common way of dealing with the unstructured nature of log data. It refers to the separation of static and dynamic part of the log message, so that both parts can be accessed independently, allowing the message to be abstracted into a more structured representation. To facilitate this task, so-called message types and the corresponding matching patterns must be first discovered, and only after that can be this pattern-set used to pattern-match individual log messages in order to extract dynamic information and impose some structure on them. Because the manual discovery of message types is a tiresome and error-prone process, we have focused our research on data mining algorithms that are able to discover message types in already generated log data. Since we have identified several deficiencies of the existing algorithms, which are limiting their capabilities, we propose a novel algorithm for message type discovery addressing these deficiencies.
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