Data heterogeneity is a big issue when different health monitoring devices produce data about the same entity. Based on the data, integration is performed and different actions are taken and executed. This paper deals with information gathering, management, and integration in a remote health management setting using ontologies. Ontologies allow for expressing and understanding a domain through common concepts. In health management settings where different devices produce data in different formats, the use of ontologies allows for resolving the heterogeneity and for mapping to a common format.

The authors designed their system with a monitor, analyze, plan, and execute (MAPE) strategy. To analyze the data, they used terms from the ontology web language (OWL-S). OWL-S is an ontology that allows for the discovery, invocation, composition, and monitoring of services. Specific tasks are defined for planning, and execution is performed via some rules defined in SPARQL. The paper outlines the different ontology and unified modeling language (UML) models used to illustrate the tasks.

Overall, the paper is good and provides a potential framework for home health management systems. The choice of SPARQL to define rules is interesting since SPARQL is a query language for resource description framework (RDF) graphs. It will be interesting to see the performance of the framework in a real setting. To summarize, this work can serve as a good reference for any researchers interested in the field of home health monitoring systems.