(ABSMSA), which uses SAGRO-Lite lightweight ontology, proposed by the
authors, along with the IoT-Lite and the Complex Event Service Ontology (CESO).
The thirteenth chapter was authored by Mahda Noura, Amélie Gyrard, Benjamin
Klotz, Raphael Troncy, Soumya Kanti Datta, and Martin Gaedke. It is focused on
the automotive industry and attempts at answering the question: How to better
understand the knowledge provided by Google results to build future “smart
vehicle-centric” applications? Authors present an exhaustive systematic literature
survey which is a basis for building a “common sense knowledge” dataset for the
automotive sector. The proposed methodology (KEAS: Knowledge Extraction for
the Automotive Sector) aims to analyze the most popular “knowledge elements,”
required to build smart vehicle applications and delivers: (1) keyphrase synonyms,
(2) synonyms used to build a corpus of scientific publications, (3) smart car
ontologies, and (4) the extraction of the most common terms for the automotive
sector.
The next chapter was authored by Regel Gonzalez-Usach, Matilde Julian,
Manuel Esteve, and Carlos E. Palau and deals with both interoperability and
Ambient Assisted Living (AAL) and Active and Healthy Aging (AHA). Here,
results from the European project ACTIVAGE, concerning benefits obtained
through the use of IoT in elderly smart homes, by enabling semantic interoperability
and cooperation across smart home clusters located in 12 different cities across
Europe are presented. The challenges that were solved in the project using real-time
semantic translation include: providing interoperability and semantic integration,
the management of massive real-time streams of IoT data.
In the fifteenth chapter, authored by Gennady Chuiko, Yaroslav Krainyk, Olga
Dvornik, and Yevhen Darnapuk, semantic provenance management for medical
data is considered. Specifically, authors consider the presence of semantic data on
different levels of a complex health monitoring system. The model of the
semantics-based system, for medical data provenance, has been proposed along
with the revision of the whole set of available technologies to employ in the
semantic engine and analysis of its behavior under different circumstances. Here,
inclusion of semantic information into the general dataflow should not only allow
evaluating data quality but also discover behavioral patterns to identify problems
inside a specific part of the system. Authors consider several scenarios that involve
different device types that measure a patient’s state.
The last part of this volume is devoted to problem-specific applications. In the
sixteenth chapter, Matthew Weber and Edward A. Lee consider semantic localization for the IoT. They base their work on an interesting observation that
Euclidean geometry and Newtonian time, with floating-point numbers, may not be
the best choice for IoT ecosystems. Hence, they survey location models from
robotics, the Internet, cyber-physical systems, and philosophy. As a result, a logical
framework, wherein a spatial ontology is defined as a model-theoretic structure, is
proposed. It is then argued that space-aware IoT services gain advantages for
privacy and interoperability when they are designed for the most abstract
spatial-ontologies possible.
Preface
ix
authors, along with the IoT-Lite and the Complex Event Service Ontology (CESO).
The thirteenth chapter was authored by Mahda Noura, Amélie Gyrard, Benjamin
Klotz, Raphael Troncy, Soumya Kanti Datta, and Martin Gaedke. It is focused on
the automotive industry and attempts at answering the question: How to better
understand the knowledge provided by Google results to build future “smart
vehicle-centric” applications? Authors present an exhaustive systematic literature
survey which is a basis for building a “common sense knowledge” dataset for the
automotive sector. The proposed methodology (KEAS: Knowledge Extraction for
the Automotive Sector) aims to analyze the most popular “knowledge elements,”
required to build smart vehicle applications and delivers: (1) keyphrase synonyms,
(2) synonyms used to build a corpus of scientific publications, (3) smart car
ontologies, and (4) the extraction of the most common terms for the automotive
sector.
The next chapter was authored by Regel Gonzalez-Usach, Matilde Julian,
Manuel Esteve, and Carlos E. Palau and deals with both interoperability and
Ambient Assisted Living (AAL) and Active and Healthy Aging (AHA). Here,
results from the European project ACTIVAGE, concerning benefits obtained
through the use of IoT in elderly smart homes, by enabling semantic interoperability
and cooperation across smart home clusters located in 12 different cities across
Europe are presented. The challenges that were solved in the project using real-time
semantic translation include: providing interoperability and semantic integration,
the management of massive real-time streams of IoT data.
In the fifteenth chapter, authored by Gennady Chuiko, Yaroslav Krainyk, Olga
Dvornik, and Yevhen Darnapuk, semantic provenance management for medical
data is considered. Specifically, authors consider the presence of semantic data on
different levels of a complex health monitoring system. The model of the
semantics-based system, for medical data provenance, has been proposed along
with the revision of the whole set of available technologies to employ in the
semantic engine and analysis of its behavior under different circumstances. Here,
inclusion of semantic information into the general dataflow should not only allow
evaluating data quality but also discover behavioral patterns to identify problems
inside a specific part of the system. Authors consider several scenarios that involve
different device types that measure a patient’s state.
The last part of this volume is devoted to problem-specific applications. In the
sixteenth chapter, Matthew Weber and Edward A. Lee consider semantic localization for the IoT. They base their work on an interesting observation that
Euclidean geometry and Newtonian time, with floating-point numbers, may not be
the best choice for IoT ecosystems. Hence, they survey location models from
robotics, the Internet, cyber-physical systems, and philosophy. As a result, a logical
framework, wherein a spatial ontology is defined as a model-theoretic structure, is
proposed. It is then argued that space-aware IoT services gain advantages for
privacy and interoperability when they are designed for the most abstract
spatial-ontologies possible.
Preface
ix