data heterogeneity. Finally, issues related to the Web of Things and how it augments the IoT concepts are also discussed.
The second chapter was prepared by Jayashree R. Prasad, Priya M. Shelke, and
Rajesh S. Prasad. They introduce the basic ideas, concepts, and technologies of the
Semantic Web. Authors illustrate these concepts in the context of implementations
of Semantic Web desktop and geospatial Semantic Web, as applied to agriculture,
health care, and IoT in general. Finally, issues related to data provenance are also
considered, specifically within the scope of the PROV data model.
The third chapter has been authored by Reinaldo Padilha França, Ana Carolina
Borges Monteiro, Rangel Arthur, and Yuzo Iano. They have focused their attention
on semantic searches. Specifically, they provide an overview of the Semantic Web
and technology behind the Semantic Web search engines.
In the fourth chapter of this part, Hemanta Kumar Palo considers the fact that there
are three different aspects related to the IoT paradigm: (i) things, (ii) Internet, and
(iii) semantics. With this in mind, the author reviews and emphasizes the key
emerging trends of the semantic technology impacting the IoT. Particularly, the work
focuses on different aspects of information modeling, ontology design, semantic
interoperability, machine learning, security policy, and processing of semantic data.
Finally, in the last chapter of the first part, Rajiv Pandey and Mrinal Pande
discuss the concepts of provenance and trustability, by outlining available models
of trust and tools for trust management. Additionally, the chapter introduces an
example of trust implementation in an existing ontology, using provenance assertions based on the PROV-DM, proposed by the World Wide Web consortium.
The fact that the second part of this book is devoted to IoT data and its interoperability should not be surprising. It has been argued many times that interoperability is one of the roadblocks of faster uptake of IoT solutions. As a matter of
fact, in 2016, the European Commission has funded six independent research
projects devoted to IoT interoperability. Keeping this in mind, let us summarize the
next six chapters.
The sixth chapter, authored by Arunima Sharma and Ramesh Babu Battula,
focuses on complexity and variance in data materializing in IoT deployments,
which make it difficult to apply and query available data, to realize user-centered
applications. Here, authors propose to use ontologies to resolve issues in data
naming. The most popular IoT and selected application domain ontologies are
described, and their use is discussed.
The next chapter has been prepared by Amélie Gyrard, Ghislain Atemezing, and
Martin Serrano. They recognize heterogeneity of (1) data format, (2) languages to
describe sensor metadata, (3) models for structuring sensor datasets, (4) reasoning
mechanisms and rule languages to interpret sensor datasets, and (5) applications. In
this context, innovative methodologies, to link and associate the data from different
domains to improve knowledge discovery, have been discussed. In this context, the
chapter is focused on the ontology quality when building sensor-based applications
and describes the PerfectO, a Knowledge Directory Services tool. PerfectO assists
ontology designers to improve ontologies to be reused in other projects. It selects
Preface
vii
The second chapter was prepared by Jayashree R. Prasad, Priya M. Shelke, and
Rajesh S. Prasad. They introduce the basic ideas, concepts, and technologies of the
Semantic Web. Authors illustrate these concepts in the context of implementations
of Semantic Web desktop and geospatial Semantic Web, as applied to agriculture,
health care, and IoT in general. Finally, issues related to data provenance are also
considered, specifically within the scope of the PROV data model.
The third chapter has been authored by Reinaldo Padilha França, Ana Carolina
Borges Monteiro, Rangel Arthur, and Yuzo Iano. They have focused their attention
on semantic searches. Specifically, they provide an overview of the Semantic Web
and technology behind the Semantic Web search engines.
In the fourth chapter of this part, Hemanta Kumar Palo considers the fact that there
are three different aspects related to the IoT paradigm: (i) things, (ii) Internet, and
(iii) semantics. With this in mind, the author reviews and emphasizes the key
emerging trends of the semantic technology impacting the IoT. Particularly, the work
focuses on different aspects of information modeling, ontology design, semantic
interoperability, machine learning, security policy, and processing of semantic data.
Finally, in the last chapter of the first part, Rajiv Pandey and Mrinal Pande
discuss the concepts of provenance and trustability, by outlining available models
of trust and tools for trust management. Additionally, the chapter introduces an
example of trust implementation in an existing ontology, using provenance assertions based on the PROV-DM, proposed by the World Wide Web consortium.
The fact that the second part of this book is devoted to IoT data and its interoperability should not be surprising. It has been argued many times that interoperability is one of the roadblocks of faster uptake of IoT solutions. As a matter of
fact, in 2016, the European Commission has funded six independent research
projects devoted to IoT interoperability. Keeping this in mind, let us summarize the
next six chapters.
The sixth chapter, authored by Arunima Sharma and Ramesh Babu Battula,
focuses on complexity and variance in data materializing in IoT deployments,
which make it difficult to apply and query available data, to realize user-centered
applications. Here, authors propose to use ontologies to resolve issues in data
naming. The most popular IoT and selected application domain ontologies are
described, and their use is discussed.
The next chapter has been prepared by Amélie Gyrard, Ghislain Atemezing, and
Martin Serrano. They recognize heterogeneity of (1) data format, (2) languages to
describe sensor metadata, (3) models for structuring sensor datasets, (4) reasoning
mechanisms and rule languages to interpret sensor datasets, and (5) applications. In
this context, innovative methodologies, to link and associate the data from different
domains to improve knowledge discovery, have been discussed. In this context, the
chapter is focused on the ontology quality when building sensor-based applications
and describes the PerfectO, a Knowledge Directory Services tool. PerfectO assists
ontology designers to improve ontologies to be reused in other projects. It selects
Preface
vii