Automated knowledge graph construction from raw log data

Andreas Ekelhart; Fajar J. Ekaputra; Elmar Kiesling

Automated knowledge graph construction from raw log data

Andreas Ekelhart, Fajar J. Ekaputra, Elmar Kiesling

Data, Process and Knowledge Management (Polleres)

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Konferenzband

Abstract

Logs are a crucial source of information to diagnose the health and status of systems, but their manual investigation typically does not scale well and often leads to a lack of awareness and incomplete transparency about issues. To tackle this challenge, we introduce SLOGERT, a flexible framework and workflow for automated construction of knowledge graphs from arbitrary raw log messages. To this end, we combine a variety of techniques to facilitate a knowledge-based approach to log analysis.

Originalsprache	Englisch
Titel des Sammelwerks	Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice
Untertitel des Sammelwerks	co-located with 19th International Semantic Web Conference (ISWC 2020)
Herausgeber*innen	Kerry Taylor, Rafael Goncalves, Freddy Lecue, Jun Yan
Verlag	CEUR Workshop Proceedings
Seiten	204-209
Publikationsstatus	Veröffentlicht - 2020
Veranstaltung	19th International Semantic Web Conference on Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters 2020 - Virtual, Online Dauer: 1 Nov. 2020 → 6 Nov. 2020

Publikationsreihe

Reihe	CEUR Workshop Proceedings
Nummer	2721
ISSN	1613-0073

Konferenz

Konferenz	19th International Semantic Web Conference on Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters 2020
Ort	Virtual, Online
Zeitraum	1/11/20 → 6/11/20

Bibliographische Notiz

Funding Information:
★This work was sponsored by the Austrian Science Fund (FWF) and netidee SCI-ENCE under grant P30437-N31, and the Austrian Research Promotion Agency FFG under grant 877389 (OBARIS). The authors thank the funders for their generous support. 3 https://splunk.com 4 https://logstash.net Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Publisher Copyright:
© 2020 CEUR-WS. All rights reserved.

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Zitat

Ekelhart, A., Ekaputra, F. J., & Kiesling, E. (2020). Automated knowledge graph construction from raw log data. in K. Taylor, R. Goncalves, F. Lecue, & J. Yan (Hrsg.), Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice: co-located with 19th International Semantic Web Conference (ISWC 2020) (S. 204-209). CEUR Workshop Proceedings. https://ceur-ws.org/Vol-2721

Ekelhart, Andreas ; Ekaputra, Fajar J. ; Kiesling, Elmar. / Automated knowledge graph construction from raw log data. Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice: co-located with 19th International Semantic Web Conference (ISWC 2020). Hrsg. / Kerry Taylor ; Rafael Goncalves ; Freddy Lecue ; Jun Yan. CEUR Workshop Proceedings, 2020. S. 204-209 (CEUR Workshop Proceedings; Nr. 2721).

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title = "Automated knowledge graph construction from raw log data",

abstract = "Logs are a crucial source of information to diagnose the health and status of systems, but their manual investigation typically does not scale well and often leads to a lack of awareness and incomplete transparency about issues. To tackle this challenge, we introduce SLOGERT, a flexible framework and workflow for automated construction of knowledge graphs from arbitrary raw log messages. To this end, we combine a variety of techniques to facilitate a knowledge-based approach to log analysis.",

author = "Andreas Ekelhart and Ekaputra, {Fajar J.} and Elmar Kiesling",

note = "Publisher Copyright: {\textcopyright} 2020 CEUR-WS. All rights reserved.; 19th International Semantic Web Conference on Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters 2020 ; Conference date: 01-11-2020 Through 06-11-2020",

year = "2020",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR Workshop Proceedings",

number = "2721",

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Ekelhart, A, Ekaputra, FJ & Kiesling, E 2020, Automated knowledge graph construction from raw log data. in K Taylor, R Goncalves, F Lecue & J Yan (Hrsg.), Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice: co-located with 19th International Semantic Web Conference (ISWC 2020). CEUR Workshop Proceedings, CEUR Workshop Proceedings, Nr. 2721, S. 204-209, 19th International Semantic Web Conference on Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters 2020, Virtual, Online, 1/11/20. <https://ceur-ws.org/Vol-2721>

Automated knowledge graph construction from raw log data. / Ekelhart, Andreas; Ekaputra, Fajar J.; Kiesling, Elmar.
Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice: co-located with 19th International Semantic Web Conference (ISWC 2020). Hrsg. / Kerry Taylor; Rafael Goncalves; Freddy Lecue; Jun Yan. CEUR Workshop Proceedings, 2020. S. 204-209 (CEUR Workshop Proceedings; Nr. 2721).

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Konferenzband

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T1 - Automated knowledge graph construction from raw log data

AU - Ekelhart, Andreas

AU - Ekaputra, Fajar J.

AU - Kiesling, Elmar

PY - 2020

Y1 - 2020

N2 - Logs are a crucial source of information to diagnose the health and status of systems, but their manual investigation typically does not scale well and often leads to a lack of awareness and incomplete transparency about issues. To tackle this challenge, we introduce SLOGERT, a flexible framework and workflow for automated construction of knowledge graphs from arbitrary raw log messages. To this end, we combine a variety of techniques to facilitate a knowledge-based approach to log analysis.

AB - Logs are a crucial source of information to diagnose the health and status of systems, but their manual investigation typically does not scale well and often leads to a lack of awareness and incomplete transparency about issues. To tackle this challenge, we introduce SLOGERT, a flexible framework and workflow for automated construction of knowledge graphs from arbitrary raw log messages. To this end, we combine a variety of techniques to facilitate a knowledge-based approach to log analysis.

UR - http://www.scopus.com/inward/record.url?scp=85096224818&partnerID=8YFLogxK

M3 - Contribution to conference proceedings

AN - SCOPUS:85096224818

T3 - CEUR Workshop Proceedings

SP - 204

EP - 209

BT - Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice

A2 - Taylor, Kerry

A2 - Goncalves, Rafael

A2 - Lecue, Freddy

A2 - Yan, Jun

PB - CEUR Workshop Proceedings

T2 - 19th International Semantic Web Conference on Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters 2020

Y2 - 1 November 2020 through 6 November 2020

ER -

Ekelhart A, Ekaputra FJ , Kiesling E. Automated knowledge graph construction from raw log data. in Taylor K, Goncalves R, Lecue F, Yan J, Hrsg., Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice: co-located with 19th International Semantic Web Conference (ISWC 2020). CEUR Workshop Proceedings. 2020. S. 204-209. (CEUR Workshop Proceedings; Nr. 2721).