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A Cloud-based Interaction Management System Architecture for Mobile Devices

机译:基于云的移动设备交互管理系统架构

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The number of interruptions people experience on a daily basis has grown considerably over the last decade and this growth has not shown any signs of subsiding. In fact, with the exponential growth of mobile computing, interruptions are permeating the user experience. Systems must be developed to manage interruptions by reasoning about ideal timings of interactions and determining appropriate notification formats.In this work, an architecture for a cloud-based interruption management system for mobile device users is presented. The system draws from rich contextual information from the mobile device (i.e., user, task and environment dimensions) and real-time observations of the user's activities and then reasons about ideal times to interact with the user. The reasoning component (interruption algorithm) is situated in the cloud and implemented using a novel machine learning technique (an Adaptive Neuro Fuzzy Inference System). This research addresses the complex problem of determining the precise time to interact with a mobile device user and in so doing aims to reduce the negative aspects of interruptions. This paper also presents a new interruption taxonomy built on an existing framework, and a report on the current prototype developed.
机译:在过去的十年中,人们每天遇到的中断次数已经大大增加,并且这种增长没有任何缓解的迹象。实际上,随着移动计算的指数级增长,中断正在渗透到用户体验中。必须开发系统以通过合理地考虑交互的合理时间并确定适当的通知格式来管理中断。在这项工作中,提出了一种针对移动设备用户的基于云的中断管理系统的体系结构。该系统从移动设备中获取丰富的上下文信息(即用户,任务和环境维度)以及对用户活动的实时观察,然后得出与用户进行交互的理想时间的原因。推理组件(中断算法)位于云中,并使用新型机器学习技术(自适应神经模糊推理系统)实现。这项研究解决了确定与移动设备用户互动的准确时间这一复杂问题,其目的是减少干扰的负面影响。本文还介绍了基于现有框架的新的中断分类法,以及有关当前原型开发的报告。

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