{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90757"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90757","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automatically extracting interaction and app data from mobile application traces","abstract":"In this research, we used an existing system to collect mobile interaction traces and extract meaningful information in terms of interaction data, apps, and layout information and complexity of mobile apps. The preeminent driving force for this research was to come up with a system that is scalable and can be used to extract interactions and layouts from mobile apps, as well as enable us to make claims about the complexity of mobile apps and the flows that they offer. Throughout the course of this research, we collected Android mobile interaction traces and presented a technique which enables extraction of frequent interactive elements from the traces in an unsupervised manner using neural network auto-encoders and k-means clustering. The research work also enables us to find similar layouts across apps and make claims about the location of some of these interactive elements. This research provides a scalable data-driven approach to finding clusters of frequent icons and interactions as well as layouts.","abstract_html":"In this research, we used an existing system to collect mobile interaction traces and extract meaningful information in terms of interaction data, apps, and layout information and complexity of mobile apps. The preeminent driving force for this research was to come up with a system that is scalable and can be used to extract interactions and layouts from mobile apps, as well as enable us to make claims about the complexity of mobile apps and the flows that they offer. Throughout the course of this research, we collected Android mobile interaction traces and presented a technique which enables extraction of frequent interactive elements from the traces in an unsupervised manner using neural network auto-encoders and k-means clustering. The research work also enables us to find similar layouts across apps and make claims about the location of some of these interactive elements. This research provides a scalable data-driven approach to finding clusters of frequent icons and interactions as well as layouts.","abstract_has_math":false,"creators":["Harish, Abhishek"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kumar, Ranjitha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:27:21Z","date_published":"2016-07-07T20:27:21Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Unsupervised Clustering","Human-computer interaction (HCI)","Interaction Mining","Element Extraction"],"languages":["en"],"rights":["Copyright 2016 Abhishek Harish"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90757","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kumar, Ranjitha"]},{"key":"dc:creator","label":"Author","values":["Harish, Abhishek"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:27:21Z","2018-07-08T09:15:09Z","2016-04-15","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Unsupervised Clustering","Human-computer interaction (HCI)","Interaction Mining","Element Extraction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Abhishek Harish"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90757"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this research, we used an existing system to collect mobile interaction traces and extract meaningful information in terms of interaction data, apps, and layout information and complexity of mobile apps. 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This research provides a scalable data-driven approach to finding clusters of frequent icons and interactions as well as layouts.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Abhishek Harish, accepted the attached license on 2016-04-14 at 15:22.","The student, Abhishek Harish, submitted this Thesis for approval on 2016-04-14 at 15:27.","This Thesis was approved for publication on 2016-04-15 at 13:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9229 on 2016-07-07 at 13:49:16","Made available in DSpace on 2016-07-07T20:27:21Z (GMT). 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The preeminent driving force for this research was to come up with a system that is scalable and can be used to extract interactions and layouts from mobile apps, as well as enable us to make claims about the complexity of mobile apps and the flows that they offer. Throughout the course of this research, we collected Android mobile interaction traces and presented a technique which enables extraction of frequent interactive elements from the traces in an unsupervised manner using neural network auto-encoders and k-means clustering. The research work also enables us to find similar layouts across apps and make claims about the location of some of these interactive elements. This research provides a scalable data-driven approach to finding clusters of frequent icons and interactions as well as layouts.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Abhishek Harish, accepted the attached license on 2016-04-14 at 15:22.","The student, Abhishek Harish, submitted this Thesis for approval on 2016-04-14 at 15:27.","This Thesis was approved for publication on 2016-04-15 at 13:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9229 on 2016-07-07 at 13:49:16","Made available in DSpace on 2016-07-07T20:27:21Z (GMT). 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