Developing IoT Spark-Streaming Applications by Means of StreamLoader

Ferrari, Luca; Valtolina, Stefano; Mesiti, Marco

doi:10.1007/978-3-030-02849-7_11

Cited by 1 publication

(1 citation statement)

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“…The facilities so far described have been implemented in the context of the StreamLoader system. In [27] we have shown the algorithm for translating the obtained DAP in Spark streaming and reported some experiments on the scalability and efficiency of the obtained script. This system is specifically tailored for domain experts that need to develop different kinds of analysis on streams of events produced by sensors belonging to cross-domain platforms.…”

Section: The Streamloader Prototypementioning

confidence: 99%

Ontology-Based Consistent Specification of Sensor Data Acquisition Plans in Cross-Domain IoT Platforms

2019

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Nowadays there is an high number of IoT applications that seldom can interact with each other because developed within different Vertical IoT Platforms that adopt different standards. Several efforts are devoted to the construction of cross-layered frameworks that facilitate the interoperability among cross-domain IoT platforms for the development of horizontal applications. Even if their realization poses different challenges across all layers of the network stack, in this paper we focus on the interoperability issues that arise at the data management layer. Specifically, starting from a flexible multi-granular Spatio-Temporal-Thematic data model according to which events generated by different kinds of sensors can be represented, we propose a Semantic Virtualization approach according to which the sensors belonging to different IoT platforms and the schema of the produced event streams are described in a Domain Ontology, obtained through the extension of the well-known Semantic Sensor Network ontology. Then, these sensors can be exploited for the creation of Data Acquisition Plans by means of which the streams of events can be filtered, merged, and aggregated in a meaningful way. A notion of consistency is introduced to bind the output streams of the services contained in the Data Acquisition Plan with the Domain Ontology in order to provide a semantic description of its final output. When these plans meet the consistency constraints, it means that the data they handle are well described at the Ontological level and thus the data acquisition process over passed the interoperability barriers occurring in the original sources. The facilities of the StreamLoader prototype are finally presented for supporting the user in the Semantic Virtualization process and for the construction of meaningful Data Acquisition Plans.INDEX TERMS Interoperability in IoT domain, ETL operations, semantic consistency.

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Section: The Streamloader Prototypementioning

confidence: 99%