AdaM : adapting multi-user interfaces for collaborative environments in real-time
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Developing cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows realtime applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study.
Park , S , Gebhardt , C , Rädle , R , Feit , A , Vrzakova , H , Dayama , N , Yeo , H S , Klokmose , C , Quigley , A J , Oulasvirta , A & Hilliges , O 2018 , AdaM : adapting multi-user interfaces for collaborative environments in real-time . in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18) . , 184 , ACM , New York, NY , ACM CHI 2018 Conference on Human Factors in Computing Systems , Montréal , Canada , 21/04/18 . https://doi.org/10.1145/3173574.3173758conference
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18)
© 2018 Copyright is held by the owner/author(s). This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1145/3173574.3173758
DescriptionThis work was supported in part by ERC Grants OPTINT (StG2016-717054) and Computed (StG-2014-637991), SNF Grant (200021L 153644), the Aarhus University Research Foundation, the Innovation Fund Denmark (CIBIS 1311-00001B), and the Scottish Informatics and Computer Science Alliance (SICSA).
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