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Mining logs for long-term patterns. / Novikov, B.; Michailova, E.; Vasilik, D.; Ivannikova, E.; Pigul, A.

Mining logs for long-term patterns. 2013. p. 57-70.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research

Harvard

Novikov, B, Michailova, E, Vasilik, D, Ivannikova, E & Pigul, A 2013, Mining logs for long-term patterns. in Mining logs for long-term patterns. pp. 57-70. https://doi.org/10.3233/978-1-61499-161-8-57

APA

Novikov, B., Michailova, E., Vasilik, D., Ivannikova, E., & Pigul, A. (2013). Mining logs for long-term patterns. In Mining logs for long-term patterns (pp. 57-70) https://doi.org/10.3233/978-1-61499-161-8-57

Vancouver

Novikov B, Michailova E, Vasilik D, Ivannikova E, Pigul A. Mining logs for long-term patterns. In Mining logs for long-term patterns. 2013. p. 57-70 https://doi.org/10.3233/978-1-61499-161-8-57

Author

Novikov, B. ; Michailova, E. ; Vasilik, D. ; Ivannikova, E. ; Pigul, A. / Mining logs for long-term patterns. Mining logs for long-term patterns. 2013. pp. 57-70

BibTeX

@inproceedings{acfd9dd6033c465fa8df7c0a366fec41,
title = "Mining logs for long-term patterns",
abstract = "The discovery of high-level long-term system behavior patterns is essential for several tasks such as system analysis, performance tuning, adaptive data placement in a cloud data centers. This paper describes techniques for mining long-term activities from database query logs. We describe algorithms for extraction of query groups with similar occurrence patterns, identification of periodic groups, and experimentally evaluate the correspondence of the query groups with business processes in the system.",
keywords = "Pattern mining, log mining, period detection",
author = "B. Novikov and E. Michailova and D. Vasilik and E. Ivannikova and A. Pigul",
year = "2013",
doi = "10.3233/978-1-61499-161-8-57",
language = "English",
isbn = "9781614991601",
pages = "57--70",
booktitle = "Mining logs for long-term patterns",

}

RIS

TY - GEN

T1 - Mining logs for long-term patterns

AU - Novikov, B.

AU - Michailova, E.

AU - Vasilik, D.

AU - Ivannikova, E.

AU - Pigul, A.

PY - 2013

Y1 - 2013

N2 - The discovery of high-level long-term system behavior patterns is essential for several tasks such as system analysis, performance tuning, adaptive data placement in a cloud data centers. This paper describes techniques for mining long-term activities from database query logs. We describe algorithms for extraction of query groups with similar occurrence patterns, identification of periodic groups, and experimentally evaluate the correspondence of the query groups with business processes in the system.

AB - The discovery of high-level long-term system behavior patterns is essential for several tasks such as system analysis, performance tuning, adaptive data placement in a cloud data centers. This paper describes techniques for mining long-term activities from database query logs. We describe algorithms for extraction of query groups with similar occurrence patterns, identification of periodic groups, and experimentally evaluate the correspondence of the query groups with business processes in the system.

KW - Pattern mining

KW - log mining

KW - period detection

U2 - 10.3233/978-1-61499-161-8-57

DO - 10.3233/978-1-61499-161-8-57

M3 - Conference contribution

SN - 9781614991601

SP - 57

EP - 70

BT - Mining logs for long-term patterns

ER -

ID: 7369708