Laurent Bihanic

Processing data as they arrive has recently gained momentum to mine continuous, high-volume and unbounded sequence of data streams. Due to the heterogeneity and the multi-modality of this data, RDF is widely used to provide a unified metadata layer in streaming context. In response to this ever-increasing demand, a number of systems and languages were produced, aiming at RDF stream processing (RSP). However, most of them adopt a centralized execution approach which puts a barrier to ensure correct behavior and high scalability under certain circumstances such as concurrent queries and increasing input load. Only few systems sought to distribute processing, but their implementation is still in its infancy. None of them provide a full-fledged and production-ready RSP engine that is easy-to-use, supports all SPARQL 1.1 operators and adapted to industrial needs. As a solution, we present a distributed, fault-tolerant and scalable RSP system that exploits the Apache Storm framework.

As many cities around the world provide access to raw public data along the Open Data movement, many questions arise concerning the accessibility of these data. Various data formats, duplicate identifiers, heterogeneous metadata schema descriptions, and diverse means to access or query the data exist. These factors make it difficult for consumers to reuse and integrate data sources to develop innovative applications. The Semantic Web provides a global solution to these problems by providing languages and protocols for describing and accessing datasets. This paper presents Datalift, a framework and a platform helping to lift raw data sources to semantic interlinked data sources.