UniParser: A Unified Log Parser for Heterogeneous Log Data-Reference-Cited by-同舟云学术

UniParser: A Unified Log Parser for Heterogeneous Log Data

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Liu Yudong¹,Zhang Xu¹,He Shilin¹,Zhang Hongyu²,Li Liqun¹,Kang Yu¹,Xu Yong¹,Ma Minghua¹,Lin Qingwei¹,Dang Yingnong³,Rajmohan Saravan⁴,Zhang Dongmei¹

Affiliation:

1. Microsoft Research Asia, China

2. The University of Newcastle, Australia

3. Microsoft Azure, USA

4. Microsoft 365, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3511993

Reference30 articles.

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2. Michael Chow , David Meisner , Jason Flinn , Daniel Peek , and Thomas F Wenisch . 2014. The mystery machine: End-to-end performance analysis of large-scale internet services . In 11th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 14). 217–231. Michael Chow, David Meisner, Jason Flinn, Daniel Peek, and Thomas F Wenisch. 2014. The mystery machine: End-to-end performance analysis of large-scale internet services. In 11th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 14). 217–231.

3. Hetong Dai , Heng Li , Che Shao Chen , Weiyi Shang , and Tse-Hsun Chen . 2020 . Logram: Efficient log parsing using n-gram dictionaries . IEEE Transactions on Software Engineering( 2020). Hetong Dai, Heng Li, Che Shao Chen, Weiyi Shang, and Tse-Hsun Chen. 2020. Logram: Efficient log parsing using n-gram dictionaries. IEEE Transactions on Software Engineering(2020).

4. Spell: Streaming Parsing of System Event Logs

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