In many applications, such as social networks, production lines or stock analysis, its is quite essential to create and process large amount of data at high rates. Such continuous and unbounded flow of data is abstracted under the concept of data streams, and data items within such streams are generally called as events. Traditional Data stream processing techniques -- that are based on the relational models -- deal with a wide variety of applications, however, heterogeneity among the data sources makes it difficult to provide a generalised solution. Following the trend of using RDF as a unified data model for integrating diverse data sources across heterogeneous domains, Semantic stream processing and Complex Event Processing employ the RDF data model to handle and analyse complex temporal relations over high volume of RDF graph streams. However the notion of time is not considered explicitly for the RDF graph model, thus the traditional RDF graph processing techniques, that rely on offline/online indexing, are not directly applicable in this setting. In this talk, first I will introduce the basic concepts of stream processing and complex event processing. Second, I will discuss our solution for a customise RDF graph processing, while considering the streaming settings. Third, I will discuss our new query language, an extension of SPARQL, which provides new constructs and executional semantics to support complex temporal reasoning over a set of RDF graph streams.
(dans le cadre d'un recrutement post-Doc)
