{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105842"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105842","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Curation and privacy in mobile application UI repositories","abstract":"Mobile interaction mining allows everyday interaction data to be mined for insights into the best performing design patterns, usability problems, and overall design trends. So far, this data has primarily come from automated application exploration or crowdworkers completing smartphone tasks as part of a study. Both of these methods have a primary issue that the interaction patterns do not quite align with how everyday users interact with applications. However, mining interaction traces that contain personally identifiable information from real users presents a problem, mainly when that data is to be published. This thesis provides an exploratory look at the data curation and privacy considerations required to share mobile application interaction data publicly. Regarding the privacy side, we will focus on applications in the Finance and Health categories.","abstract_html":"Mobile interaction mining allows everyday interaction data to be mined for insights into the best performing design patterns, usability problems, and overall design trends. So far, this data has primarily come from automated application exploration or crowdworkers completing smartphone tasks as part of a study. Both of these methods have a primary issue that the interaction patterns do not quite align with how everyday users interact with applications. However, mining interaction traces that contain personally identifiable information from real users presents a problem, mainly when that data is to be published. This thesis provides an exploratory look at the data curation and privacy considerations required to share mobile application interaction data publicly. 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So far, this data has primarily come from automated application exploration or crowdworkers completing smartphone tasks as part of a study. Both of these methods have a primary issue that the interaction patterns do not quite align with how everyday users interact with applications. However, mining interaction traces that contain personally identifiable information from real users presents a problem, mainly when that data is to be published. This thesis provides an exploratory look at the data curation and privacy considerations required to share mobile application interaction data publicly. 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