Data-Driven User Complaint Prediction for Mobile Access Networks

Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predic...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Pan, Huimin [verfasserIn] Zhou, Sheng Jia, Yunjian Niu, Zhisheng Zheng, Meng Geng, Lu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	data-driven complaint prediction complaint location network management machine learning pipeline

Anmerkung:	© Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018

Übergeordnetes Werk:	Enthalten in: Journal of communications and information networks - [Singapore] : Springer Singapore, 2017, 3(2018), 3 vom: Sept., Seite 9-19
Übergeordnetes Werk:	volume:3 ; year:2018 ; number:3 ; month:09 ; pages:9-19

Links:	Volltext

DOI / URN:	10.1007/s41650-018-0025-2

Katalog-ID:	SPR038277948

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520			\|a Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance.
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allfields	10.1007/s41650-018-0025-2 doi (DE-627)SPR038277948 (SPR)s41650-018-0025-2-e DE-627 ger DE-627 rakwb eng Pan, Huimin verfasserin aut Data-Driven User Complaint Prediction for Mobile Access Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018 Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance. data-driven complaint prediction (dpeaa)DE-He213 complaint location (dpeaa)DE-He213 network management (dpeaa)DE-He213 machine learning pipeline (dpeaa)DE-He213 Zhou, Sheng aut Jia, Yunjian aut Niu, Zhisheng aut Zheng, Meng aut Geng, Lu aut Enthalten in Journal of communications and information networks [Singapore] : Springer Singapore, 2017 3(2018), 3 vom: Sept., Seite 9-19 (DE-627)884378101 (DE-600)2891434-X 2509-3312 nnns volume:3 year:2018 number:3 month:09 pages:9-19 https://dx.doi.org/10.1007/s41650-018-0025-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 3 2018 3 09 9-19
spelling	10.1007/s41650-018-0025-2 doi (DE-627)SPR038277948 (SPR)s41650-018-0025-2-e DE-627 ger DE-627 rakwb eng Pan, Huimin verfasserin aut Data-Driven User Complaint Prediction for Mobile Access Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018 Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance. data-driven complaint prediction (dpeaa)DE-He213 complaint location (dpeaa)DE-He213 network management (dpeaa)DE-He213 machine learning pipeline (dpeaa)DE-He213 Zhou, Sheng aut Jia, Yunjian aut Niu, Zhisheng aut Zheng, Meng aut Geng, Lu aut Enthalten in Journal of communications and information networks [Singapore] : Springer Singapore, 2017 3(2018), 3 vom: Sept., Seite 9-19 (DE-627)884378101 (DE-600)2891434-X 2509-3312 nnns volume:3 year:2018 number:3 month:09 pages:9-19 https://dx.doi.org/10.1007/s41650-018-0025-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 3 2018 3 09 9-19
allfields_unstemmed	10.1007/s41650-018-0025-2 doi (DE-627)SPR038277948 (SPR)s41650-018-0025-2-e DE-627 ger DE-627 rakwb eng Pan, Huimin verfasserin aut Data-Driven User Complaint Prediction for Mobile Access Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018 Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance. data-driven complaint prediction (dpeaa)DE-He213 complaint location (dpeaa)DE-He213 network management (dpeaa)DE-He213 machine learning pipeline (dpeaa)DE-He213 Zhou, Sheng aut Jia, Yunjian aut Niu, Zhisheng aut Zheng, Meng aut Geng, Lu aut Enthalten in Journal of communications and information networks [Singapore] : Springer Singapore, 2017 3(2018), 3 vom: Sept., Seite 9-19 (DE-627)884378101 (DE-600)2891434-X 2509-3312 nnns volume:3 year:2018 number:3 month:09 pages:9-19 https://dx.doi.org/10.1007/s41650-018-0025-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 3 2018 3 09 9-19
allfieldsGer	10.1007/s41650-018-0025-2 doi (DE-627)SPR038277948 (SPR)s41650-018-0025-2-e DE-627 ger DE-627 rakwb eng Pan, Huimin verfasserin aut Data-Driven User Complaint Prediction for Mobile Access Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018 Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance. data-driven complaint prediction (dpeaa)DE-He213 complaint location (dpeaa)DE-He213 network management (dpeaa)DE-He213 machine learning pipeline (dpeaa)DE-He213 Zhou, Sheng aut Jia, Yunjian aut Niu, Zhisheng aut Zheng, Meng aut Geng, Lu aut Enthalten in Journal of communications and information networks [Singapore] : Springer Singapore, 2017 3(2018), 3 vom: Sept., Seite 9-19 (DE-627)884378101 (DE-600)2891434-X 2509-3312 nnns volume:3 year:2018 number:3 month:09 pages:9-19 https://dx.doi.org/10.1007/s41650-018-0025-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 3 2018 3 09 9-19
allfieldsSound	10.1007/s41650-018-0025-2 doi (DE-627)SPR038277948 (SPR)s41650-018-0025-2-e DE-627 ger DE-627 rakwb eng Pan, Huimin verfasserin aut Data-Driven User Complaint Prediction for Mobile Access Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018 Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance. data-driven complaint prediction (dpeaa)DE-He213 complaint location (dpeaa)DE-He213 network management (dpeaa)DE-He213 machine learning pipeline (dpeaa)DE-He213 Zhou, Sheng aut Jia, Yunjian aut Niu, Zhisheng aut Zheng, Meng aut Geng, Lu aut Enthalten in Journal of communications and information networks [Singapore] : Springer Singapore, 2017 3(2018), 3 vom: Sept., Seite 9-19 (DE-627)884378101 (DE-600)2891434-X 2509-3312 nnns volume:3 year:2018 number:3 month:09 pages:9-19 https://dx.doi.org/10.1007/s41650-018-0025-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 3 2018 3 09 9-19
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source	Enthalten in Journal of communications and information networks 3(2018), 3 vom: Sept., Seite 9-19 volume:3 year:2018 number:3 month:09 pages:9-19
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author	Pan, Huimin
spellingShingle	Pan, Huimin misc data-driven complaint prediction misc complaint location misc network management misc machine learning pipeline Data-Driven User Complaint Prediction for Mobile Access Networks
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topic	misc data-driven complaint prediction misc complaint location misc network management misc machine learning pipeline
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title	Data-Driven User Complaint Prediction for Mobile Access Networks
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title_full	Data-Driven User Complaint Prediction for Mobile Access Networks
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title_sort	data-driven user complaint prediction for mobile access networks
title_auth	Data-Driven User Complaint Prediction for Mobile Access Networks
abstract	Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance. © Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018
abstractGer	Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance. © Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018
abstract_unstemmed	Abstract In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance. © Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018
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title_short	Data-Driven User Complaint Prediction for Mobile Access Networks
url	https://dx.doi.org/10.1007/s41650-018-0025-2
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author2	Zhou, Sheng Jia, Yunjian Niu, Zhisheng Zheng, Meng Geng, Lu
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By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">data-driven complaint prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">complaint location</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">network management</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning pipeline</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Sheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jia, 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