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Conference papers
BIGFile: Bayesian Information Gain for Fast File Retrieval
Abstract : We introduce BIGFile, a new fast file retrieval technique based on the Bayesian Information Gain framework. BIGFile provides interface shortcuts to assist the user in navigating to a desired target (file or folder). BIGFile's split interface combines a traditional list view with an adaptive area that displays shortcuts to the set of file paths estimated by our computa-tionally efficient algorithm. Users can navigate the list as usual, or select any part of the paths in the adaptive area. A pilot study of 15 users informed the design of BIGFile, revealing the size and structure of their file systems and their file retrieval practices. Our simulations show that BIGFile outper-forms Fitchett et al.'s AccessRank, a best-of-breed prediction algorithm. We conducted an experiment to compare BIGFile with ARFile (AccessRank instantiated in a split interface) and with a Finder-like list view as baseline. BIGFile was by far the most efficient technique (up to 44% faster than ARFile and 64% faster than Finder), and participants unanimously preferred the split interfaces to the Finder.
Cited literature [40 references]
https://hal.archives-ouvertes.fr/hal-01791754
Contributor : Wanyu Liu <>
Submitted on : Monday, May 14, 2018 - 6:10:51 PM
Last modification on : Tuesday, April 21, 2020 - 1:08:28 AM
Document(s) archivé(s) le : Tuesday, September 25, 2018 - 2:19:59 AM
Contributor : Wanyu Liu <>
Submitted on : Monday, May 14, 2018 - 6:10:51 PM
Last modification on : Tuesday, April 21, 2020 - 1:08:28 AM
Document(s) archivé(s) le : Tuesday, September 25, 2018 - 2:19:59 AM
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