In resource-constrained devices, adaptation of data stream processing to variations of data rates, availability of resources and environment changes is crucial for consistency and continuity of running applications. Context-aware adaptation, as a new dimension of research in data stream mining, enhances and optimizes distributed data stream processing tasks. Context-awareness is one of the key aspects of ubiquitous computing as applicationsC¸ successful operations rely on detecting changes and adjusting accordingly. This paper presents a general architecture for context-aware adaptive mining of data streams that aims to dynamically and autonomously adjust data stream mining parameters according to changes in context and resource availability in distributed and heterogeneous computing environments.