Abstract
In the field of wireless network optimization, with the enlargement of network size and the complication of network structure, traditional processing methods cannot effectively identify the causes of network faults in the face of increasing network data. In this paper, we propose a root-cause-analysis method based on distributed data mining (DRCA). Firstly, we put forward an improved decision tree, where the selection of the best split-feature is based on the feature’s purity-gain, and then we skillfully convert the problem of root-cause-analysis into modeling of an improved decision tree and interpretation of the tree model. In order to solve the problem of memory and efficiency associated with large-scale data, we parallelize the algorithm and distribute the tasks to multiple computers. The experiments show that DRCA is an effective, efficient, and scalable method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Method and system for optimising the performance of a network. https://www.google.com/patents/US20040266442
Ye Ouyang, M., Fallah, H.: A performance analysis for UMTS packet switched network based on multivariate KPIS. In: IJNGN, pp. 80–92 (2010)
Julisch, K.: Clustering intrusion detection alarms to support root cause analysis. ACM Trans. Inf. Syst. Secur. 6(4), 443–471 (2003)
Chen, M., Zheng, A.X., Lloyd, J., Jordan, M.I., Brewer, E.: Failure diagnosis using decision trees. In: ICAC, pp. 36–43 (2004)
Lpez-Soto, D., Yacout, S., Angel-Bello, F.: Root cause analysis of familiarity biases in classification of inventory items based on logical patterns recognition. Comput. Ind. Eng. 93, 121–130 (2016)
Bauer, M., Cox, J.W., Caveness, M.H., Downs, J.J., Nina, F.: Nearest neighbors methods for root cause analysis of plantwide disturbances. In: Industrial and Engineering Chemistry Research, pp. 5977–5984 (2007)
Doggett, M.: A statistical comparison of three root cause analysis tools. J. Ind. Technol. 20(2), 2–9 (2004)
Doggett, M.: Root cause analysis: a framework for tool selection. Qual. Manag. J. 12(4), 34–45 (2005)
Kwad, N., Choi, C.H.: Input feature selection for classification problem. IEEE Trans. Neural Netw. 13(1), 143–159 (2002)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1984)
Narlikar, G.J.: A parallel, multithreaded decision tree builder. Technical report, Carnegie Mellon University (1998)
Acknowledgement
This work is sponsored by Huawei Innovation Research Program (HIRP Grant No. YB2015090007), the Natural Science Foundation of China (Grant Nos. 61673204, 61321491), the Program for Distinguished Talents of Jiangsu Province, China (Grant No. 2013-XXRJ-018), and the Fundamental Research Funds for the Central Universities (Grant No. 020214380026).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Fan, S., Yang, Y., Lu, W., Song, P. (2017). Distributed Data Mining for Root Causes of KPI Faults in Wireless Networks. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-63564-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63563-7
Online ISBN: 978-3-319-63564-4
eBook Packages: Computer ScienceComputer Science (R0)