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A Case-Based Component Selection Framework for Mobile Context-Aware Applications

机译：基于案例的移动上下文感知应用程序的组件选择框架

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摘要

In the ever-changing pervasive computing paradigm, applications, especially those running on resource-scarce mobile devices, have to adapt to the runtime environment as the users are roaming around. Various adaptation techniques, relying on dynamic composition of components, have been proposed by a number of researchers. Nevertheless, most existing approaches only support component selection based on predefined rules and strategies. Because of the limitation of pure rule-based approach, context-awareness can not be well supported. In this paper, we propose a software component selection framework for mobile pervasive computing. Our approach adopts the case-based reasoning technique to provide proactive component selection. Context-awareness and personalization are embodied in the reasoning and selection process. As a proof of concept, we developed and evaluated a context-aware personal communicator (CAPC) application using adaptive component selection, with a synthesized execution trace obtained from real-life e-mail softwares ported to CAPC. Our results show that the adaptive component selection can reduce maximum memory consumption by at least 20%, and the context-guided reasoning technique can improve reasoning accuracy by nearly 10% within acceptable reasoning time.
机译：在不断变化的普遍计算范例中，应用程序，尤其是在资源稀缺移动设备上运行的应用程序，必须适应用户漫游的运行时环境。通过许多研究人员提出了各种适应技术，依赖于组件的动态组成。然而，大多数现有方法只支持基于预定义规则和策略的组件选择。由于基于纯规则的方法的限制，因此无法得到很好的支持背景感知。在本文中，我们提出了一种用于移动普及计算的软件组件选择框架。我们的方法采用基于案例的推理技术提供了主动组件选择。上下文意识和个性化体现在推理和选择过程中。作为概念的证据，我们开发并评估了使用自适应组件选择的上下文感知个人通信器（CAPC）应用程序，其中从移植到CAPC的真实电子邮件软件中获得的合成执行跟踪。我们的研究结果表明，自适应组件选择可以将最大存储器消耗降低至少20％，并且上下文推理技术可以在可接受的推理时间内通过近10％提高推理准确性。

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