Читать книгу Computational Statistics in Data Science - Группа авторов - Страница 109

Performing streaming data analysis over ontologies and linked open data are a challenging and emerging research area. Semantic web technology, an extension of the World Wide Web, is used to improve the interoperability of heterogeneous sources with a data model called Resource Description Framework (RDF) and ontological languages such as Web Ontology Language (OWL). Some of the works done using ontology or linked open data on data stream include [97–99]. Due to the dynamic nature of data stream, current solutions for reasoning over the data model and ontological languages are not suited to streaming data context. This gap brought about what is referred to as stream reasoning. Stream reasoning is the set of inference approaches and deduction mechanisms concerned with the provision of continuous inference over a data stream, leading to a better decision support system [100]. Stream reasoning has been applied in remote health monitoring [101], smart cities [102], semantic analysis of social media [103], maritime safety, and securities [104], amongst others. Another attempt to improve semantic web ontology is to lift the existing streams to RDF streams using intuitive configuration mechanisms. Some of the techniques for RDF stream modeling include Semantic Sensor Network (SSN) ontology [105], Stream Annotation Ontology (SOA) [106], smart appliances reference (SAREF) ontology [107], and Linked Stream Annotation Engine (LSane) [108].