{"id":3590,"date":"2018-09-02T08:58:44","date_gmt":"2018-09-02T08:58:44","guid":{"rendered":"http:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3590"},"modified":"2020-09-04T07:45:21","modified_gmt":"2020-09-04T07:45:21","slug":"data-driven-user-complaint-prediction-for-mobile-access-networks","status":"publish","type":"post","link":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3590","title":{"rendered":"Data-Driven User Complaint Prediction for Mobile Access Networks"},"content":{"rendered":"<p><span class=\"paper_subtitle\"><a href=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2018\/10\/JCIN-2018-00036.pdf\" rel=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2018\/10\/JCIN-2018-00036.pdf\">Pan18<\/a><\/span><\/p>\n<p><span class=\"paper_subtitle\">LANGUAGE<\/span> English<\/p>\n<p><span class=\"paper_subtitle\">SOURCE<\/span> <strong><em> Journal of Communications and Information Networks, Vol.3, No.3, Sept. 2018<\/em><\/strong><\/p>\n<p><span class=\"paper_subtitle\">Published Date<\/span>:2018-09<\/p>\n<p>Abstract\u2014In 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<br \/>\ndown-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile<br \/>\noperator, 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p><a href=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2018\/10\/JCIN-2018-00036.pdf\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-117\" title=\"pdf\" src=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2010\/08\/pdf.gif\"alt=\"\" width=\"95\" height=\"50\" \/><\/a>Huimin Pan, Sheng Zhou, Yunjian Jia, Zhisheng Niu, Meng Zheng, Lu Geng, Data-Driven User Complaint Prediction for Mobile Access Networks, <span class=\"papersource\">Journal of Communications and Information Networks, Vol.3, No.3, Sept. 2018<\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[7],"tags":[143,100],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3590"}],"collection":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3590"}],"version-history":[{"count":1,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3590\/revisions"}],"predecessor-version":[{"id":3592,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3590\/revisions\/3592"}],"wp:attachment":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3590"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3590"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3590"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}