The growing number of Internet of Things (IoT) devices provide a massive pool of sensing data. However, turning data into actionable insights is not a trivial task, especially in the context of IoT, where application development itself is complex. The process entails working with heterogeneous devices via various communication protocols to co-ordinate and fetch datasets, followed by a series of data transformations. Graphical mashup tools, based on the principles of flow-based programming paradigm, operating at a higher-level of abstraction are in widespread use to support rapid prototyping of IoT applications. Nevertheless, the current state-of-the-art mashup tools suffer from several architectural limitations which prevent composing in-flow data analytics pipelines. In response to this, the paper contributes by (i) designing novel flow-based programming concepts based on the actor model to support data analytics pipelines in mashup tools, prototyping the ideas in a new mashup tool called aFlux and providing a detailed comparison with the existing state-of-the-art and (ii) enabling easy prototyping of streaming applications in mashup tools by abstracting the behavioural configurations of stream processing via graphical flows and validating the ease as well as the effectiveness of composing stream processing pipelines from an end-user perspective in a traffic simulation scenario.

越来越多的物联网（IoT）设备提供了大量的传感数据。但是，将数据转化为可行的见解并不是一件容易的事，尤其是在物联网的情况下，因为物联网的应用程序开发本身很复杂。该过程需要通过各种通信协议使用异构设备来协调和获取数据集，然后进行一系列数据转换。基于基于流的编程范例原理的图形化混搭工具在更高的抽象级别上运行，被广泛使用以支持IoT应用程序的快速原型设计。尽管如此，当前最先进的混搭工具仍然受到一些体系结构上的限制，这些局限性阻止了组成流入数据分析管道的工作。对此，