Federation of heterogeneous Sample Clauses

Federation of heterogeneous data streams using semantic description and annotation. Internet of Things research in recent years has focused on modelling domain knowledge of sensor networks and services. The SSN ontology (Xxxxxxx Xxxxxxx, 2012) is one of the most significant efforts in development of an information model for sensory data. The SSN Ontology provides a vocabulary for describing concepts, such as sensors, outputs, observation value and feature of interests. Most notable extensions include ontologies for coastal features, services and roles for emergency response. However, although the SSN ontology defines a high-level scheme of sensor systems, it does not include representation of aggregation/summarisation of observation and measurement data. IoT-A model (Xxxxxxx Xx, 2012) and IoT.est semantic representations (Xxx Xxxx, 2012) describe how to enhance the utilisation and representation of domain knowledge in sensor networks: the former provided an architectural base for further IoT projects, and the latter enhanced the IoT-A model with some service and test concepts. The Observation and Measurement (O&M) descriptions for sensory data are also described as a part of the Sensor Web Enablement (SWE) standards (Xxxx Xxxxx, 2008) from the Open Geospatial Consortium (OGC). While it provides several important syntactic descriptions, due to the fact that it is based on XML, it has a weak semantic structure for expressing knowledge, and lacks some important features to describe ontology in more detail. There has been a recent study to improve the semantic richness of the O&M ontology where authors transformed it into Ontology Web Language (OWL) representation (Xxxx X. Xxxxxx, 2009). However, the O&M ontology continues to not only lack temporal features to represent time-series observations in detail, but also semantics for our purposes due to the straightforward approach that has been used in the process. Another very similar approach has been carried out in (Xxxx X. Xxxxxx, 2009), in which all the XML tags of O&M ontology have been mapped into OWL concepts. However, although the authors present how to access the annotated data through SPARQL queries, these queries are not efficient for the applications that need to access sensor data in real time, as the SPARQL queries generates significant traffic if the sampling rate is high Consequently, there still remains a need for a framework to handle real-time semantic annotation as well as efficient knowledge representation of sensory dat...
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