IoT Semantic Interoperability for Active and Healthy Ageing
337
Data Lake, a data analytics module and a set of deployment, development, management and visualization tools. The Data Lake allows access to the raw data collected
by the devices, as well as the metadata used by the Data Analytics component. The
Data Lake does not store the raw data from the sensors. Instead, it retrieves the data
from the different platforms and makes use of the SIL to translate the data into the
common ontology. Also, the Data Lake provides a metadata storage service for the
parameters of the trained models produced and used by the data analytics module.
The Data Analytics module is built on top of the Data Lake and provides a set of
predictive data analytics methods that can be used on the data. These methods allow
comparing parameters values against patterns or expected values in order to detect
differences, changes or deviations. The results of these analyses can be retrieved from
a REST API or represented using the visualization tools included in the Service Layer.
Finally, the Service Layer includes several tools for the management of the devices
and platforms of the DS and a set of development tools.
AIoTES API: all the components of AIoTES are accessible through a common
API that allows a homogeneous and secure access to all its functionalities. This
common API facilitates the development of an ecosystem of new applications and
services based on the combination or exchange of data from different IoT platforms.
3.5 Application and Impact of the Semantic Framework
The enablement of semantic interoperability allows the improvement and exchange of
services, as well as the creation of new services based on interoperability. AIoTES can
translate the data in real time from one platform to another, which allows platformspecific applications and services to understand and use data coming from a different
platform. Moreover, AIoTES offers a set platform-independent services that can be
used by any DS or application. The following sections describe these new services.
3.5.1 Services Provided by AIoTES
The Service Layer of AIoTES provides a set of platform-independent services that
rely on the semantic interoperability enabled by the SIL. Among these, the most
important are the Data Lake services and the Data Analytics methods.
Data Lake
The Data Lake provides access to the raw data collected by the devices in a common
format and semantics. The intended use of this component is not to store sensor
data but to retrieve data from the platforms and make use of the SIL to translate it
into the common ontology. Nevertheless, the Data Lake also provides storage for
platforms unable to store their own data (for instance, platforms deployed in devices
with storage restrictions). Hence, the Data Lake allows access to data from any DS in
337
Data Lake, a data analytics module and a set of deployment, development, management and visualization tools. The Data Lake allows access to the raw data collected
by the devices, as well as the metadata used by the Data Analytics component. The
Data Lake does not store the raw data from the sensors. Instead, it retrieves the data
from the different platforms and makes use of the SIL to translate the data into the
common ontology. Also, the Data Lake provides a metadata storage service for the
parameters of the trained models produced and used by the data analytics module.
The Data Analytics module is built on top of the Data Lake and provides a set of
predictive data analytics methods that can be used on the data. These methods allow
comparing parameters values against patterns or expected values in order to detect
differences, changes or deviations. The results of these analyses can be retrieved from
a REST API or represented using the visualization tools included in the Service Layer.
Finally, the Service Layer includes several tools for the management of the devices
and platforms of the DS and a set of development tools.
AIoTES API: all the components of AIoTES are accessible through a common
API that allows a homogeneous and secure access to all its functionalities. This
common API facilitates the development of an ecosystem of new applications and
services based on the combination or exchange of data from different IoT platforms.
3.5 Application and Impact of the Semantic Framework
The enablement of semantic interoperability allows the improvement and exchange of
services, as well as the creation of new services based on interoperability. AIoTES can
translate the data in real time from one platform to another, which allows platformspecific applications and services to understand and use data coming from a different
platform. Moreover, AIoTES offers a set platform-independent services that can be
used by any DS or application. The following sections describe these new services.
3.5.1 Services Provided by AIoTES
The Service Layer of AIoTES provides a set of platform-independent services that
rely on the semantic interoperability enabled by the SIL. Among these, the most
important are the Data Lake services and the Data Analytics methods.
Data Lake
The Data Lake provides access to the raw data collected by the devices in a common
format and semantics. The intended use of this component is not to store sensor
data but to retrieve data from the platforms and make use of the SIL to translate it
into the common ontology. Nevertheless, the Data Lake also provides storage for
platforms unable to store their own data (for instance, platforms deployed in devices
with storage restrictions). Hence, the Data Lake allows access to data from any DS in