research-article

An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis

Authors:

Jianfeng Zhang,

Patrick P.C. Lee,

Keli ZhangAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1951 - 1959

https://doi.org/10.1145/3097983.3098120

Published: 13 August 2017 Publication History

Abstract

With the advent of cellular network technologies, mobile Internet access becomes the norm in everyday life. In the meantime, the complaints made by subscribers about unsatisfactory cellular network access also become increasingly frequent. From a network operator's perspective, achieving accurate and timely cellular network diagnosis about the causes of the complaints is critical for both improving subscriber-perceived experience and maintaining network robustness. We present the Intelligent Customer Care Assistant (ICCA), a distributed fault classification system that exploits a data-driven approach to perform large-scale cellular network diagnosis. ICCA takes massive network data as input, and realizes both offline model training and online feature computation to distinguish between user and network faults in real time. ICCA is currently deployed in a metropolitan LTE network in China that is serving around 50 million subscribers. We show via evaluation that ICCA achieves high classification accuracy (85.3%) and fast query response time (less than 2.3 seconds). We also report our experiences learned from the deployment.

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Cited By

Shi XOsinski MQian CWang J(2022)Towards automatic troubleshooting for user-level performance degradation in cellular servicesProceedings of the 28th Annual International Conference on Mobile Computing And Networking10.1145/3495243.3560535(716-728)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1145/3495243.3560535
Shi XQian CSheoran AWang J(2022)ML-based Cellular Service Issue Troubleshooting Using Limited Ground Truth Data2022 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN)10.1109/LANMAN54755.2022.9820585(1-2)Online publication date: 11-Jul-2022
https://doi.org/10.1109/LANMAN54755.2022.9820585
Li RCheng ZLee PWang PQiang YLan LHe CLu JWang MDing X(2021)Automated Intelligent Healing in Cloud-Scale Data Centers2021 40th International Symposium on Reliable Distributed Systems (SRDS)10.1109/SRDS53918.2021.00032(244-253)Online publication date: Oct-2021
https://doi.org/10.1109/SRDS53918.2021.00032
Show More Cited By

Index Terms

An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining
2. Networks
  1. Network services
    1. Network monitoring

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 August 2017

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KDD '17

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Shi XOsinski MQian CWang J(2022)Towards automatic troubleshooting for user-level performance degradation in cellular servicesProceedings of the 28th Annual International Conference on Mobile Computing And Networking10.1145/3495243.3560535(716-728)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1145/3495243.3560535
Shi XQian CSheoran AWang J(2022)ML-based Cellular Service Issue Troubleshooting Using Limited Ground Truth Data2022 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN)10.1109/LANMAN54755.2022.9820585(1-2)Online publication date: 11-Jul-2022
https://doi.org/10.1109/LANMAN54755.2022.9820585
Li RCheng ZLee PWang PQiang YLan LHe CLu JWang MDing X(2021)Automated Intelligent Healing in Cloud-Scale Data Centers2021 40th International Symposium on Reliable Distributed Systems (SRDS)10.1109/SRDS53918.2021.00032(244-253)Online publication date: Oct-2021
https://doi.org/10.1109/SRDS53918.2021.00032
Hu JZhou ZYang XMalone JWilliams JBhagwan RPorter G(2020)CableMonProceedings of the 17th Usenix Conference on Networked Systems Design and Implementation10.5555/3388242.3388287(619-632)Online publication date: 25-Feb-2020
https://dl.acm.org/doi/10.5555/3388242.3388287
Ochiai KSenkawa KYamamoto NTanaka YFukazawa YTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Real-time On-Device Troubleshooting Recommendation for SmartphonesProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330669(2783-2791)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330669

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