Discovering Critical Factors Affecting RDF Stores Success
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amount of information concerning its products, customers, productive resources.
These information can be then (also in near real time) processed to optimize the
production resources usage or to increase customer satisfaction. Nonetheless, the
implementation of this capability is only the first needed item towards the strengthening of the approach proposed by Apps4ME. In addition to it, the scenarios analyzed
in the project [35] pose the need of specific key-features, such as the capability to
guarantee different security users profiles (corresponding to different access levels
to the resources) and the capability to manage various historical versions of data
(as a version management system) [23]. Finally, since the enterprise typically owns
different type of information, it must be capable to handle multimodal information
(e.g. text, structured information, audios, images, other binary files, etc.).
3.1.2 PEGASO Case Study
PEGASO is an European research project which aims the improvement of motivation
and self-awareness of the teenagers towards a healthful lifestyle [24]. In particular,
the project aims at reducing their obesity-related risks as behavioural habits of the
teenagers can significantly affect their health status when become adults.
One of the challenges of the project is the handling of large amounts of heterogeneous data coming from various sources (e.g. sensors, apps, etc.). In order to face
this issue, a solution has been conceived and adopted based on a common shared
data model, which aims at representing a conceptualization of the overall PEGASO
knowledge. On the basis of the SWT, it is defined a meta-model expressed under the
form of application ontology that allows to capture the obesity-related features of
teenagers, thus enabling formally organizing, searching and sharing their behavioural
information. In this regards, the application ontology provides a representation of
the main concepts and the relationship among these concepts in the specific analyzed
domain. To manage this ontology, a solution of cloud-based RDF store is needed.
For this reason, a significant stage of this project has been focused on the identification and adoption of an efficient RDF store capable to manage semantic data, also
under the form of big data. These data that must be managed include: (a) an application ontology (the meta-mode), which represent the knowledge about teenagers
behaviours; (b) the ontological individuals, which adhere to the domain ontology;
(c) the inference entailment needed to derive new knowledge. The major required
features for an ad hoc RDF store comprises the capability to manage both data in
motion and data at rest, to guarantee the security of these data (most of the handled
data is sensitive). In addition, it is important that RDF store provides the capability
of reasoning in order to allow to infer new information, leveraging the data acquired
from sensors and apps which monitor the behaviours of the teenagers. Finally, spatiotemporal data, generated to monitor teenagers, must be properly managed.
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amount of information concerning its products, customers, productive resources.
These information can be then (also in near real time) processed to optimize the
production resources usage or to increase customer satisfaction. Nonetheless, the
implementation of this capability is only the first needed item towards the strengthening of the approach proposed by Apps4ME. In addition to it, the scenarios analyzed
in the project [35] pose the need of specific key-features, such as the capability to
guarantee different security users profiles (corresponding to different access levels
to the resources) and the capability to manage various historical versions of data
(as a version management system) [23]. Finally, since the enterprise typically owns
different type of information, it must be capable to handle multimodal information
(e.g. text, structured information, audios, images, other binary files, etc.).
3.1.2 PEGASO Case Study
PEGASO is an European research project which aims the improvement of motivation
and self-awareness of the teenagers towards a healthful lifestyle [24]. In particular,
the project aims at reducing their obesity-related risks as behavioural habits of the
teenagers can significantly affect their health status when become adults.
One of the challenges of the project is the handling of large amounts of heterogeneous data coming from various sources (e.g. sensors, apps, etc.). In order to face
this issue, a solution has been conceived and adopted based on a common shared
data model, which aims at representing a conceptualization of the overall PEGASO
knowledge. On the basis of the SWT, it is defined a meta-model expressed under the
form of application ontology that allows to capture the obesity-related features of
teenagers, thus enabling formally organizing, searching and sharing their behavioural
information. In this regards, the application ontology provides a representation of
the main concepts and the relationship among these concepts in the specific analyzed
domain. To manage this ontology, a solution of cloud-based RDF store is needed.
For this reason, a significant stage of this project has been focused on the identification and adoption of an efficient RDF store capable to manage semantic data, also
under the form of big data. These data that must be managed include: (a) an application ontology (the meta-mode), which represent the knowledge about teenagers
behaviours; (b) the ontological individuals, which adhere to the domain ontology;
(c) the inference entailment needed to derive new knowledge. The major required
features for an ad hoc RDF store comprises the capability to manage both data in
motion and data at rest, to guarantee the security of these data (most of the handled
data is sensitive). In addition, it is important that RDF store provides the capability
of reasoning in order to allow to infer new information, leveraging the data acquired
from sensors and apps which monitor the behaviours of the teenagers. Finally, spatiotemporal data, generated to monitor teenagers, must be properly managed.