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Query for Streaming Information: Dynamic Processing and Adaptive Incremental Maintenance of RDF Stream

Recently with dynamic information being ubiquitous on the Web, there have been efforts to extend RDF and SPARQL for representing streaming information and continuous querying functionalities, respectively. While existing works focusing on formalization and implementation of continuous querying process over RDF streams, little attention has deserved the problem of querying the complex temporal correlations among RDF stream tuples and the effective, scalable implementation of RDF stream processing system. To fill this gap, in this paper we propose CT-SPARQL and AIMRS, a language specifying for the compositional stream patterns and an architecture for adaptive incremental maintenance of RDF stream tuples defined in CT-SPARQL. We believe that this work will benefit a wide range of real-time analyzing and future predicting applications.