作者：
Mahapatra, TanmayaTech Univ Munich
Inst Med Informat Stat & Epidemiol IMedIS Sch Med Ismaninger Str 22 D-81675 Munich Germany Tech Univ Munich
Software & Syst Engn Res Grp Boltzmannstr 03 D-85748 Garching Germany
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 ...
详细信息
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 streamprocessing via graphical flows and validating the ease as well as the effectiveness of composing streamprocessing pipelines from an end-user perspective in a traffic simulation scenario.
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