Abstract

The technological advances in the Internet-of-Things (IoT) have led to the generation of large amounts of data and the production of a large number of IoT platforms for their management. The abundance of raw data necessitates the use of data analytics in order to extract useful patterns for decision making. Current architectures for big data analytics in the IoT domain address the large volume and velocity of the produced data. However, they do not address the semantic heterogeneity in the data models used by diverse IoT platforms, which emerges when large-scale deployments, spanning across multiple deployment sites, are considered. This paper proposes an architecture for big data analytics in the context of large-scale IoT systems consisting of multiple IoT platforms. A Semantic Interoperability Layer (SIL) handles the interoperability among the data models of the individual platforms, using semantic mappings between them and a unified ontology. Data queries to the SIL and result collection is handled by a cloud-based data management layer, namely the Data Lake, along with storage of metadata needed by data analytics methods. Based on this infrastructure, web-based data analytics and visual analytics methods are used to analyze the collected data, while being agnostic of platform-specific details. The proposed architecture is developed in the context of healthcare provision for older people, although it can be applied to any IoT domain.

Highlights

  • The technological advances of the Internet-of-Things (IoT) have led to the development of human-centric IoT applications, such as e-Health and intelligent transportation systems

  • The current paper aims to contribute to this direction, by proposing an architecture for large-scale IoT data analytics, based on semantic interoperability across diverse IoT platforms

  • This paper proposes an architecture for big data analytics in the IoT domain, in the context of large-scale federations of IoT platforms with heterogeneous data models

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Summary

Introduction

The technological advances of the Internet-of-Things (IoT) have led to the development of human-centric IoT applications, such as e-Health and intelligent transportation systems. In large-scale applications, spanning a large number of different installations, maybe across different countries, each installation may use a different IoT platform, having its own data model for describing the IoT devices and collected data. The architecture is developed in the context of the ACTIVAGE project [1], whose goal is to support large-scale IoT applications in deployment sites across European countries, in order to exploit the large volume of collected data. Towards this goal, existing IoT platforms already deployed in different sites are used, as well as various sensing systems, such as the behavioural monitoring systems developed in the FrailSafe project [2].

Big data analytics
Semantic interoperability
The ACTIVAGE data analytics architecture
Semantic interoperability layer
Data Lake
Data analytics and information visualization
Methods
Preliminary evaluation
Conclusion and next steps
Full Text
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