Stream analytics in IoT mashup tools

Tanmaya Mahapatra; Christian Prehofer; Ilias Gerostathopoulos; Ioannis Varsamidakis

doi:10.1109/VLHCC.2018.8506548

Stream analytics in IoT mashup tools

Tanmaya Mahapatra, Christian Prehofer, Ilias Gerostathopoulos, Ioannis Varsamidakis

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Consumption of data streams generated from IoT devices during IoT application development is gaining prominence as the data insights are paramount for building high-impact applications. IoT mashup tools, i.e. tools that aim to reduce the development effort in the context of IoT via graphical flow-based programming, suffer from various architectural limitations which prevent the usage of data analytics as part of the application logic. Moreover, the approach of flow-based programming is not conducive for stream processing. We introduce our new mashup tool aFlux based on actor system with concurrent and asynchronous execution semantics to overcome the prevalent architectural limitations and support in-built user-configurable stream processing capabilities. Furthermore, parametrizing the control points of stream processing in the tool enables non-experts to use various stream processing styles and deal with the subtle nuances of stream processing effortlessly. We validate the effectiveness of parametrization in a real-time traffic use case.

Original language	English
Title of host publication	Proceedings - 2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018
Editors	Caitlin Kelleher, Gregor Engels, Joao Paulo Fernandes, Jacome Cunha, Jorge Mendes
Publisher	IEEE Computer Society
Pages	227-231
Number of pages	5
ISBN (Electronic)	9781538642351
DOIs	https://doi.org/10.1109/VLHCC.2018.8506548
Publication status	Published - 23 Oct 2018
Externally published	Yes
Event	2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018 - Lisbon, Portugal Duration: 1 Oct 2018 → 4 Oct 2018

Publication series

Name	Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC
Volume	2018-October
ISSN (Print)	1943-6092
ISSN (Electronic)	1943-6106

Conference

Conference	2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018
Country/Territory	Portugal
City	Lisbon
Period	1/10/18 → 4/10/18

Funding

This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of Economic Affairs, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.

Funders	Funder number
Bavarian Ministry of Economic Affairs, Energy and Technology
Bavarian State Government
Bayerisches Staatsministerium für Wirtschaft, Infrastruktur, Verkehr und Technologie
Technische Universität München

Keywords

End-users
Graphical flows
Internet of Things
IoT mashup tools
Stream analytics

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10.1109/VLHCC.2018.8506548

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Mahapatra, T., Prehofer, C., Gerostathopoulos, I., & Varsamidakis, I. (2018). Stream analytics in IoT mashup tools. In C. Kelleher, G. Engels, J. P. Fernandes, J. Cunha, & J. Mendes (Eds.), Proceedings - 2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018 (pp. 227-231). Article 8506548 (Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/VLHCC.2018.8506548

Mahapatra, Tanmaya ; Prehofer, Christian ; Gerostathopoulos, Ilias et al. / Stream analytics in IoT mashup tools. Proceedings - 2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018. editor / Caitlin Kelleher ; Gregor Engels ; Joao Paulo Fernandes ; Jacome Cunha ; Jorge Mendes. IEEE Computer Society, 2018. pp. 227-231 (Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC).

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abstract = "Consumption of data streams generated from IoT devices during IoT application development is gaining prominence as the data insights are paramount for building high-impact applications. IoT mashup tools, i.e. tools that aim to reduce the development effort in the context of IoT via graphical flow-based programming, suffer from various architectural limitations which prevent the usage of data analytics as part of the application logic. Moreover, the approach of flow-based programming is not conducive for stream processing. We introduce our new mashup tool aFlux based on actor system with concurrent and asynchronous execution semantics to overcome the prevalent architectural limitations and support in-built user-configurable stream processing capabilities. Furthermore, parametrizing the control points of stream processing in the tool enables non-experts to use various stream processing styles and deal with the subtle nuances of stream processing effortlessly. We validate the effectiveness of parametrization in a real-time traffic use case.",

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Mahapatra, T, Prehofer, C, Gerostathopoulos, I & Varsamidakis, I 2018, Stream analytics in IoT mashup tools. in C Kelleher, G Engels, JP Fernandes, J Cunha & J Mendes (eds), Proceedings - 2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018., 8506548, Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, vol. 2018-October, IEEE Computer Society, pp. 227-231, 2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018, Lisbon, Portugal, 1/10/18. https://doi.org/10.1109/VLHCC.2018.8506548

Stream analytics in IoT mashup tools. / Mahapatra, Tanmaya; Prehofer, Christian; Gerostathopoulos, Ilias et al.
Proceedings - 2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018. ed. / Caitlin Kelleher; Gregor Engels; Joao Paulo Fernandes; Jacome Cunha; Jorge Mendes. IEEE Computer Society, 2018. p. 227-231 8506548 (Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC; Vol. 2018-October).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Mahapatra T, Prehofer C, Gerostathopoulos I, Varsamidakis I. Stream analytics in IoT mashup tools. In Kelleher C, Engels G, Fernandes JP, Cunha J, Mendes J, editors, Proceedings - 2018 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2018. IEEE Computer Society. 2018. p. 227-231. 8506548. (Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC). doi: 10.1109/VLHCC.2018.8506548

Stream analytics in IoT mashup tools

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