IoT in Provenance Management of Medical Data
351
• Depression;
• Asthma;
• Obesity.
The review of general-purpose ontologies shows that LOV4IoT assembled 67
ontologies (the highest value among all catalogs) developed in the period from 2006
to 2018. It is also shown that proposed systems or models employ a diverse set of technologies. They vary by wireless connections, communication protocols, operating
systems, target end-user devices, supported sensors, and deployment into computational infrastructure. Often projects from the catalog deal only with a single specific
problem, e.g., Electro-Cardiograms (ECG) and their inclusion into other structure
might be concerned with significant efforts. That fact, once again, proves that to cope
with heterogeneity of the ontologies, it is necessary to provide extension interfaces
so they can be reused for different purposes.
Researches on the topic of healthcare that involve IoT and semantic tools provide
huge advances for the quality of life of patients. However, the observation regarding
these researches is that they typically deal with single patient types having a particular
type of disease (e.g. dementia, heart diseases) or peculiar data type for analysis (e.g.
ECG). Therefore, a combination of the findings into a universal system is concerned
with additional expenses of development time. Analysis of the resources also allows
making an assumption about an architectural solution where devices can send data to
the cloud environment and further activities take place in the cloud. The feedback is
provided as a result of the analysis of the cloud tools or by an expert himself/herself.
Electronic healthcare systems exploit different data formats for storage and
communications. When it comes to annotation of the raw data, it is necessary that
data representation should support addition of semantic data at least as an ad-hoc
function. If the protocol message cannot be altered with semantic data, it makes it
unfeasible to use this protocol in the core of semantic system. Now, let us provide
the review of the common data formats for healthcare systems and analysis of their
suitability for mentioned purposes.
The most common problem associated with this process is that it increases overall
size of the message that is important for lightweight applications. In more details
attachment of the information to medical data is discussed hereinafter.
3 Medical Data Provenance
Despite the fact that semantic-enabled healthcare systems are one of the most
common examples of technology application, the problem of medical data provenance has not gained necessary attention yet.
As data provenance is a general problem not only for the healthcare system, but
also for other scientific fields that that face bulk amount of information, multiple
endeavors were made in this direction. First, the W3C organization issued PROV
Ontology (PROV-O) to deal with common issues of data provenance. The ontology
351
• Depression;
• Asthma;
• Obesity.
The review of general-purpose ontologies shows that LOV4IoT assembled 67
ontologies (the highest value among all catalogs) developed in the period from 2006
to 2018. It is also shown that proposed systems or models employ a diverse set of technologies. They vary by wireless connections, communication protocols, operating
systems, target end-user devices, supported sensors, and deployment into computational infrastructure. Often projects from the catalog deal only with a single specific
problem, e.g., Electro-Cardiograms (ECG) and their inclusion into other structure
might be concerned with significant efforts. That fact, once again, proves that to cope
with heterogeneity of the ontologies, it is necessary to provide extension interfaces
so they can be reused for different purposes.
Researches on the topic of healthcare that involve IoT and semantic tools provide
huge advances for the quality of life of patients. However, the observation regarding
these researches is that they typically deal with single patient types having a particular
type of disease (e.g. dementia, heart diseases) or peculiar data type for analysis (e.g.
ECG). Therefore, a combination of the findings into a universal system is concerned
with additional expenses of development time. Analysis of the resources also allows
making an assumption about an architectural solution where devices can send data to
the cloud environment and further activities take place in the cloud. The feedback is
provided as a result of the analysis of the cloud tools or by an expert himself/herself.
Electronic healthcare systems exploit different data formats for storage and
communications. When it comes to annotation of the raw data, it is necessary that
data representation should support addition of semantic data at least as an ad-hoc
function. If the protocol message cannot be altered with semantic data, it makes it
unfeasible to use this protocol in the core of semantic system. Now, let us provide
the review of the common data formats for healthcare systems and analysis of their
suitability for mentioned purposes.
The most common problem associated with this process is that it increases overall
size of the message that is important for lightweight applications. In more details
attachment of the information to medical data is discussed hereinafter.
3 Medical Data Provenance
Despite the fact that semantic-enabled healthcare systems are one of the most
common examples of technology application, the problem of medical data provenance has not gained necessary attention yet.
As data provenance is a general problem not only for the healthcare system, but
also for other scientific fields that that face bulk amount of information, multiple
endeavors were made in this direction. First, the W3C organization issued PROV
Ontology (PROV-O) to deal with common issues of data provenance. The ontology