IoT in Provenance Management of Medical Data
349
This paper is organized as follows. The next section introduces review of contemporary scientific sources on the latest developments in the field of semantic technologies, IoT, and healthcare. Then, according to the analysis, we devise general recommendations for semantic-based healthcare system that utilizes IoT data. Finally, we
demonstrate the use-case of data provenance for Melatonin-sulfate measurements.
2 Review of Related Work
In his paper [8], T. Burners-Lee envisioned Semantic Web as Web 3.0 and outlined the
most significant advantages of inclusion of semantic data into existing web description. The leading outcome of this should be a transition in inter-machine communication that can be established on the semantic level rather than explicit communication
using software commands. However, the pace of this innovation is not enough to
declare semantic web as a ubiquitous technology and new developments are yet to
come. The main culprit for this is, once again, a heterogeneity of approaches and
semantic frameworks.
On the other side, valuable achievements provided in scientific researches and
practical applications cannot be denied. First, standards for semantic data representation were developed. They include Resource Description Framework (RDF),
Web Ontology Language (OWL), SPARQL Protocol, and RDF Query Language
(SPARQL) among the most useful ones. They will be discussed later. Second,
complex semantic frameworks and systems were designed to address problems of
data management, analysis, system control in various fields. Healthcare system is
not left overlooked and multiple research works are devoted to this field.
RDF constitutes basics for the storage and linking of semantics. All information
described in RDF-format is stored as “triples”. Triple consists of three elements:
• Subject
• Relationship
• Object.
Single triple defines how subject and object relate to each other. Element defined
in one triple can be referenced from another one. Complex networks or graphs are
formed this way. Being quite simple data representation format, triples provide a
powerful instrument to define specific knowledge or activity domain when combined
into a graph structure.
OWL is facilitates creation of ontologies [9]. While it is very similar to the RDF,
OWL can be regarded as a higher-level entity that general rules for the specific
domain.
SPARQL is a query language with SQL-like syntax designed specifically to query
data stored in RDF and OWL-files. SPARQL is a key software component for
processing of semantic data. SPARQL-queries can be stored as system rules that
further evolves into knowledge extraction and analysis mechanism.
349
This paper is organized as follows. The next section introduces review of contemporary scientific sources on the latest developments in the field of semantic technologies, IoT, and healthcare. Then, according to the analysis, we devise general recommendations for semantic-based healthcare system that utilizes IoT data. Finally, we
demonstrate the use-case of data provenance for Melatonin-sulfate measurements.
2 Review of Related Work
In his paper [8], T. Burners-Lee envisioned Semantic Web as Web 3.0 and outlined the
most significant advantages of inclusion of semantic data into existing web description. The leading outcome of this should be a transition in inter-machine communication that can be established on the semantic level rather than explicit communication
using software commands. However, the pace of this innovation is not enough to
declare semantic web as a ubiquitous technology and new developments are yet to
come. The main culprit for this is, once again, a heterogeneity of approaches and
semantic frameworks.
On the other side, valuable achievements provided in scientific researches and
practical applications cannot be denied. First, standards for semantic data representation were developed. They include Resource Description Framework (RDF),
Web Ontology Language (OWL), SPARQL Protocol, and RDF Query Language
(SPARQL) among the most useful ones. They will be discussed later. Second,
complex semantic frameworks and systems were designed to address problems of
data management, analysis, system control in various fields. Healthcare system is
not left overlooked and multiple research works are devoted to this field.
RDF constitutes basics for the storage and linking of semantics. All information
described in RDF-format is stored as “triples”. Triple consists of three elements:
• Subject
• Relationship
• Object.
Single triple defines how subject and object relate to each other. Element defined
in one triple can be referenced from another one. Complex networks or graphs are
formed this way. Being quite simple data representation format, triples provide a
powerful instrument to define specific knowledge or activity domain when combined
into a graph structure.
OWL is facilitates creation of ontologies [9]. While it is very similar to the RDF,
OWL can be regarded as a higher-level entity that general rules for the specific
domain.
SPARQL is a query language with SQL-like syntax designed specifically to query
data stored in RDF and OWL-files. SPARQL is a key software component for
processing of semantic data. SPARQL-queries can be stored as system rules that
further evolves into knowledge extraction and analysis mechanism.