Adopting Context Mediation in Information Integration to Resolve Semantic Heterogeneity in Distributed Environment

Ontology-based semantic information integration resolve the schema-level heterogeneity and part of data level heterogeneity between distributed data sources. But it is ubiquitous that schema semantics of information is identical while the interpretation of it varies with different context, and ontology-based semantic information integration can not resolve this context heterogeneity. By introducing context representation and context mediation to ontology based information integration, the attribute-level context heterogeneity can be detected and reconciled automatically, and hence a complete solution for semantic heterogeneity is formed. Through a concrete example, the context representation and the process in which the attribute-level context heterogeneity is reconciled during query processing are presented. This resolution can make up the deficiency of schema mapping based semantic information integration. With the architecture proposed in this paper the semantic heterogeneity solution is adaptive and extensive.

Evaluation on Information Model about Sensors Featured by Relationships to Measured Structural Objects

In accordance with the requirements of expanding Machine-To-Machine communication (M2M), the network overlay is in progress in several domains such as Smart Grid. Consequently, it is predictable that opportunities and cases of integrating yielded data from devices such as sensors will increase more. Accordingly, the importance of Ontology and Information Models (IM) which normalize the semantics including sensor expressions, have increased, and the standards of these definitions have been more important as well. So far, there have been multiple initiatives for standardizing the Ontology and IM in regards to the sensors expression such as Sensor Standards Harmonization by the National Institute of Standards and Technology (NIST), W3C Semantic Sensor Network (SSN) and the recent W3C IoT-Lite Ontology. However, there is still room to improve the current level of the Ontology and IM on the viewpoint of the implementing structure. This paper presents a set of IMs on abstract sensors and contexts in regards to the phenomenon around these sensors from the point of view of a structure implementing these specified sensors. As several previous studies have pointed out, multiple aspects on the sensors should be modeled. Accordingly, multiple sets of Ontology and IM on these sensors should be defined. Our study has intended to clarify the relationship between configurations and physical measured quantities of the structures implementing a set of sensors. Up to present, they have not been generalized and have remained unformulated. Consequently, due to the result of this analysis, it is expected to implement a more generalized translator module easily, which aggregates the measured data from the sensors on the middleware level managing these Ontology and IM, instead of the layer of user application programs.