research-article

Analysis of Measure Fluctuation Based on Adtributor Algorithm

Authors:

Zhao Yanjie,

Duan Xiaochen,

Kang LinAuthors Info & Claims

ICBDR '20: Proceedings of the 4th International Conference on Big Data Research

Pages 1 - 6

https://doi.org/10.1145/3445945.3445946

Published: 01 March 2021 Publication History

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Abstract

In order to be able to quickly and easily discover the root-cause of data when fluctuation occurs, this paper introduces an Adtributor algorithm based on the explanatory power and surprise value, and it is selected from the forecast value compared with, the calculation of non-mutually exclusive dimension and the selection of algorithm threshold three aspects to optimize Adtributor algorithm. Through two comparative experiments, it's proved that the calculation of non-mutually exclusive dimensions uses the sum of the measure corresponding to each element and the algorithm threshold compute by the cardinality of dimensions. Finally, the practical application of Adtributor algorithm in data platform is introduced.

References

[1]

Ranjita Bhagwan. Adtributor: Revenue Debugging in Advertising Systems.

Google Scholar

[2]

Sun Y, Zhao Y, Su Y, HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes[J]. IEEE Access, 2018, 6:10909-10923.

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Google Scholar

[3]

Su Y, Zhang Y, Wang Z, CoFlux: robustly correlating KPIs by fluctuations for service troubleshooting[C]// the International Symposium. 2019.

Digital Library

Google Scholar

[4]

Lin Q, Lou J G, Zhang H, iDice: problem identification for emerging issues[C]// the 38th International Conference. IEEE, 2016.

Google Scholar

[5]

Djurdjanovic, D, Hearn, C, Liu, Y. Immune Systems Inspired Approach to Anomaly Detection, Fault Localization and Diagnosis in Complex Dynamic Systems[C]// Conference on Grand Challenges in Modeling & Simulation. Society for Modeling & Simulation International, 2010.

Google Scholar

[6]

H. Yan, L. Breslau, D. Massey, D. Pei, and J. Yates.G-RCA: A Generic Root Cause Analysis Platform for Service Quality Management in Large IP Networks.In Proceedings of ACM CoNext, 2010.

Digital Library

Google Scholar

[7]

S. Sarawagi. iDiff: Informative Summarizaton of Differences in Multidimensional Aggregates. Data Mining and Knowledge Discovery, 5(4):255-276, 2001.

Digital Library

Google Scholar

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ICBDR '20: Proceedings of the 4th International Conference on Big Data Research

November 2020

110 pages

ISBN:9781450387750

DOI:10.1145/3445945

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Association for Computing Machinery

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Published: 01 March 2021

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ICBDR 2020

ICBDR 2020: 2020 the 4th International Conference on Big Data Research

November 27 - 29, 2020

Tokyo, Japan

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Complexity results for explanations in the structural-model approach

Integration of Root Cause Analysis into a Model-Based Testing Process of a Mobile Switching Server

Online root-cause performance analysis of parallel applications

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