Abstract: Over the past decade intelligent environments have grown in sophistication. Many recent paradigm shifts – such as the Internet of Things (IoT), Ambient Assisted Living (AAL), e-health and telemedicine – envision large distributed networks of intelligent devices, applications and services that are sensitive to the presence of people and responsive to their needs. Cutting edge technologies will autonomously and collectively operate on a growing volume of information arriving at ever increasing velocities to transparently and non-intrusively support users during their activities. Especially the escalating variety of information that applications have to deal with is a non-trivial concern. Making sense out of heterogeneous and pervasive streams of sensor events to anticipate and address the needs of users is a ubiquitous challenge that many interactive context-aware applications in intelligent environments frequently face. Furthermore, software solutions that continuously interpret the tasks and contexts of a variety of individuals with different needs are often faced with scalability concerns. We present SAMURAI, a batch and streaming context architecture that integrates and exposes well-known components for complex event processing, machine learning, and knowledge representation. SAMURAI builds upon key concepts of the Lambda architecture and big data enabling technologies to achieve horizontal scalability and responsive interaction with its users. Two application cases validate the feasibility and performance of our context architecture, demonstrating near-linear scalability, flexible elasticity and smooth interaction capabilities.
Keywords: Context, batch and stream processing, scalability, intelligent applications
