Combining semantic web and IoT to reason with health and safety policies

Göynügür, Emre; Şensoy, Murat; Mel, Geeth de

doi:10.1109/bigdata.2017.8258269

Cited by 3 publications

(1 citation statement)

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“…Such meta data define a context representation of the relationships of the time series with domain entities or with other time series, which can be particularly complex for IoT applications due to the potential heterogeneity of the metering data sources. It is important that intelligent data-driven applications have seamless access to the time series data along with their context information, such to exploit their full potential for gaining insights or taking decisions [7], [13], [14].…”

Section: B Time Series and Model Data Management With Contextmentioning

confidence: 99%

Castor: Contextual IoT Time Series Data and Model Management at Scale

Chen

Eck

Fusco

et al. 2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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We demonstrate Castor, a cloud-based system for contextual IoT time series data and model management at scale. Castor is designed to assist Data Scientists in (a) exploring and retrieving all relevant time series and contextual information that is required for their predictive modelling tasks; (b) seamlessly storing and deploying their predictive models in a cloud production environment; (c) monitoring the performance of all predictive models in production and (semi-)automatically retraining them in case of performance deterioration. The main features of Castor are: (1) an efficient pipeline for ingesting IoT time series data in real time; (2) a scalable, hybrid data management service for both time series and contextual data; (3) a versatile semantic model for contextual information which can be easily adapted to different application domains; (4) an abstract framework for developing and storing predictive models in R or Python; (5) deployment services which automatically train and/or score predictive models upon user-defined conditions. We demonstrate Castor for a real-world Smart Grid use case and discuss how it can be adapted to other application domains such as Smart Buildings, Telecommunications, Retail or Manufacturing.

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Section: B Time Series and Model Data Management With Contextmentioning

confidence: 99%

Castor: Contextual IoT Time Series Data and Model Management at Scale

Chen

Eck

Fusco

et al. 2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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Enabling smart environments through scalable policy reasoning and Internet of Things

Göynügür

Şensoy

Mel

2018

WIREs Data Min & Knowl

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In this paper, we discuss how to combine ontology-based policy reasoning mechanisms with in-use Internet of Things applications to customize and automate device behaviors. We discuss how the policy framework can be extended with data federation to handle diverse and distributed data sources. We demonstrate that smart devices and sensors can be orchestrated through policies in diverse settings, from smart home environments to hazardous workplaces, such as coal mines. Lastly, we evaluate our approach using real applications with real data and demonstrate that it is scalable under high load of data and devices.

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