Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.

翻译：随着众多上下文感知型物联网应用的快速发展，对上下文数据的需求比以往任何时候都更加迫切。针对上下文及上下文感知的研究正在开展，以应对诸多实际和技术挑战，拓展其应用范围。其中一个挑战是处理大量上下文查询时的性能提升问题。负责推理并向应用传递上下文的上下文管理平台，通过服务质量（QoS）参数来衡量这一问题。尽管缓存是提升QoS的有效途径，但上下文的瞬态性及其变异性、异构性等特征，带来了额外的实时成本管理问题。本文旨在对自适应数据缓存领域的最新技术进行批判性综述，以期构建成本高效且性能优良的自适应缓存策略知识体系。我们全面梳理了大量研究文献，对自适应缓存领域的不同技术、策略、方法及方案进行了评估、比较与对比。我们的批判性分析围绕将上下文自适应缓存作为核心研究问题展开。随后，我们提出了自适应上下文缓存的正式定义，并明确了设计良好、目标最优的自适应上下文缓存策略所应具备的特征与需求。