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Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,7,31]]},"abstract":"<jats:p>The service usage analysis, aiming at identifying customers\u2019 messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced framework for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage subsequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach. We find that the proposed multi-label multi-view framework can help overcome the pain of mixed-usage subsequences and can be generalized to latent activity analysis in sequential data, beyond in-App usage analytics.<\/jats:p>","DOI":"10.1145\/3151937","type":"journal-article","created":{"date-parts":[[2018,1,31]],"date-time":"2018-01-31T13:25:40Z","timestamp":1517405140000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-8024","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"first","affiliation":[{"name":"Missouri University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junming","family":"Liu","sequence":"additional","affiliation":[{"name":"Rutgers University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Rutgers University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,1,30]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Advances in Intelligent","author":"Abonyi Janos","unstructured":"Janos Abonyi , Balazs Feil , Sandor Nemeth , and Peter Arva . 2003. 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