Contextriver

Contextriver is a modular framework for collecting, processing, and delivering contextual data streams in real time. It is designed to help applications reason about their situation by composing context from multiple sources, including user state, device sensors, environmental data, and application logs. The goal is to provide a unified, time-aware view of context that can drive adaptive behavior, personalization, and automation.

The architecture centers on a context pipeline with pluggable adapters for data sources, a context graph or reasoning layer to derive higher-level beliefs, and a delivery layer to push context updates to consumers. Contextriver supports both push and pull models, event-driven pub/sub, and windowed stream processing. It emphasizes data normalization through schemas and policies, enabling interoperability and privacy controls such as access rules and data minimization.

Key features include temporal reasoning across context items, provenance tracking of data sources, conflict resolution when sources disagree, versioning of context states, and pluggable backends for storage, messaging, and analytics. The system aims to be language-agnostic and usable from different runtimes via adapters or middleware.

Typical use cases are adaptive user interfaces, smart environments, IoT analytics, workflow automation, and personalized recommendations. It is suitable for research prototypes and production deployments in organizations that require cross-source context synthesis and auditability.

Origin and status: the concept emerged in academic and open-source discussions around contextual computing and event-driven architectures; implementations vary and remain at early to mid-development stages in most projects.

See also context-aware computing, event stream processing, data provenance.