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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.", "subjects" : [ { "otype" : "Classification", "mtid" : 10034, "link" : "/api/classification/10034", "label" : "Számítás- és információtudomány", "published" : true, "snippet" : true }, { "otype" : "Classification", "mtid" : 10034, "link" : "/api/classification/10034", "label" : "Számítás- és információtudomány", "published" : true, "snippet" : true } ], "keywords" : [ { "otype" : "Keyword", "mtid" : 1735, "link" : "/api/keyword/1735", "label" : "DESIGN", "published" : true, "oldId" : 1735, "snippet" : true }, { "otype" : "Keyword", "mtid" : 2387, "link" : "/api/keyword/2387", "label" : "PERFORMANCE", "published" : true, "oldId" : 2387, "snippet" : true }, { "otype" : "Keyword", "mtid" : 15224, "link" : "/api/keyword/15224", "label" : "Internet Traffic", "published" : true, "oldId" : 15224, "snippet" : true }, { "otype" : "Keyword", "mtid" : 1062913, "link" : "/api/keyword/1062913", "label" : "experimentation", "published" : true, "oldId" : 1062913, "snippet" : true }, { "otype" : "Keyword", "mtid" : 1171784, "link" : "/api/keyword/1171784", "label" : "Implementation algorithms", "published" : true, "oldId" : 1171784, "snippet" : true }, { "otype" : "Keyword", "mtid" : 1331665, "link" : "/api/keyword/1331665", "label" : "Multi-view", "published" : true, "oldId" : 1331665, "snippet" : true }, { "otype" : "Keyword", "mtid" : 1582921, "link" : "/api/keyword/1582921", "label" : "In-App analytics", "published" : true, "snippet" : true }, { "otype" : "Keyword", "mtid" : 1582922, "link" : "/api/keyword/1582922", "label" : "service usage", "published" : true, "snippet" : true }, { "otype" : "Keyword", "mtid" : 1582923, "link" : "/api/keyword/1582923", "label" : "multi-label", "published" : true, "snippet" : true } ], "digital" : null, "printed" : null, "sourceYear" : 2019, "foreignEdition" : true, "foreignLanguage" : true, "fullPublication" : true, "conferencePublication" : false, "nationalOrigin" : null, "missingAuthor" : false, "oaType" : "NONE", "oaCheckDate" : "2020-05-02", "oaFree" : false, "citationCount" : 0, "citationCountUnpublished" : 0, "citationCountWoOther" : 0, "independentCitCountWoOther" : 0, "doiCitationCount" : 0, "wosCitationCount" : 0, "scopusCitationCount" : 0, "independentCitationCount" : 0, "unhandledCitationCount" : 0, "citingPubCount" : 0, "independentCitingPubCount" : 0, "unhandledCitingPubCount" : 0, "citedPubCount" : 1, "citedCount" : 1, "ratings" : [ { "otype" : "SjrRating", "mtid" : 10747097, "link" : "/api/sjrrating/10747097", "label" : "sjr:Q2 (2018) Scopus - Artificial Intelligence ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2157-6904 2157-6912", "listPos" : 49, "rankValue" : 0.49, "type" : "journal", "ratingType" : { "otype" : "RatingType", "mtid" : 10002, "link" : "/api/ratingtype/10002", "label" : "sjr", "code" : "sjr", "published" : true, "snippet" : true }, "subject" : { "otype" : "ClassificationExternal", "mtid" : 1702, "link" : "/api/classificationexternal/1702", "label" : "Scopus - Artificial Intelligence", "published" : true, "oldId" : 1702, "snippet" : true }, "ranking" : "Q2", "calculation" : "DIRECT", "published" : true, "snippet" : true } ], "ratingsForSort" : "Q2", "hasCitationDuplums" : false, "directInstitutesForSort" : "", "ownerAuthorCount" : 2, "ownerInstituteCount" : 6, "directInstituteCount" : 0, "authorCount" : 4, "contributorCount" : 0, "hasQualityFactor" : true, "link" : "/api/publication/30464562", "label" : "Fu Yanjie et al. 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