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Where are the RDF Streams? Deploying RDF Streams on the Web of Data with
"... Abstract. RDF Stream Processing (RSP) bridges the gap between se-mantic technologies and data stream systems. Although a number of RSP systems have been recently proposed, no RDF streams are actually made publicly available on the Web. To cope with this, RSP engines require ad-hoc wrappers in order ..."
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Abstract. RDF Stream Processing (RSP) bridges the gap between se-mantic technologies and data stream systems. Although a number of RSP systems have been recently proposed, no RDF streams are actually made publicly available on the Web. To cope with this, RSP engines require ad-hoc wrappers in order to be fed from non-RDF streams available on the Internet. In this paper we present TripleWave: an approach for pub-lishing existing streams on the Web as RDF streams, using mappings to perform live transformation of data, and following the Linked Data prin-ciples. We implemented and deployed TripleWave for a concrete use-case: a live feed of updates of Wikipedia. 1
Towards Efficient Semantically Enriched Complex Event Processing and Pattern Matching
"... Abstract. Management and recognition of event patterns is becoming thoroughly ingrained in many application areas of Semantically enabled Complex Event Pro-cessing (SCEP). However, the reliance of state-of-the-art technologies on rela-tional and RDF triple model without having the notion of time has ..."
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Abstract. Management and recognition of event patterns is becoming thoroughly ingrained in many application areas of Semantically enabled Complex Event Pro-cessing (SCEP). However, the reliance of state-of-the-art technologies on rela-tional and RDF triple model without having the notion of time has severe lim-itations. This restricts the system to employ temporal reasoning at RDF level and use historical events to predict new situations. Additionally, the semantics of traditional query languages makes it quite challenging to implement distributed event processing. In our vision, SCEP needs to consider RDF as a first class cit-izen and should implement parallel and distributed processing to deal with large amount of data streams. In this paper, we discuss various challenges and limi-tations of state-of-the-art technologies and propose a possible solution to extend RDF data model for stream processing and pattern matching. Furthermore, we describe a high-level query design that enables efficient parallel and distributed pattern matching through query rewriting. 1
Towards A Unified Language for RDF Stream Query Processing
"... Abstract. In recent years, several RDF Stream Processing (RSP) sys-tems have emerged, which allow querying RDF streams using extensions of SPARQL that include operators to take into account the velocity of this data. These systems are heterogeneous in terms of syntax, capabili-ties and evaluation se ..."
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Abstract. In recent years, several RDF Stream Processing (RSP) sys-tems have emerged, which allow querying RDF streams using extensions of SPARQL that include operators to take into account the velocity of this data. These systems are heterogeneous in terms of syntax, capabili-ties and evaluation semantics. Recently, the W3C RSP Group started to work on a common model for representing and querying RDF streams. The emergence of such a model and its accompanying query language is expected to take the most representative, significant and important features of previous efforts, but will also require a careful design and definition of its semantics. In this work, we present a proposal for the query semantics of the W3C RSP query language, and we discuss how it can capture the semantics of existing engines (CQELS, C-SPARQL, SPARQLstream), explaining and motivating their differences. Then, we use RSP-QL to analyze the current version of the W3C RSP Query Lan-guage proposal. 1
DIONYSUS: Towards Query-aware Distributed Processing of RDF Graph Streams
"... Arguably, the most significant obstacle to handle the emerging ap-plication’s data deluge is to design a system that addresses the chal-lenges for big data’s volume, velocity and variety. Work in RDF stream processing (RSP) systems partly addresses the challenge of variety by promoting the RDF model ..."
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Arguably, the most significant obstacle to handle the emerging ap-plication’s data deluge is to design a system that addresses the chal-lenges for big data’s volume, velocity and variety. Work in RDF stream processing (RSP) systems partly addresses the challenge of variety by promoting the RDF model. However, challenges like volume, velocity are overlooked by existing approaches. These challenges demand optimised combination of scale-out and scale-up solutions. Furthermore, various other requirements for RSP sys-tems, such as an efficient integration of distributed stream sources, storage of historical streams and their analysis, and integration of stateful operators to support complex event processing over streams are far from being addressed in an efficient way. Our vision is to design a general purpose RDF graph streaming system, which will be able to cope with distributed streams and shares local optimising strategies to allow different kinds of queries (analytical, streaming, sequence-based) through one query interface. The proposed sys-tem will offer a black-box solution that will allow analysts to tap in the goldmine of massive RDF graph streams. We consider the challenges and opportunities associated in designing such system, introduce our approaches to these topics, and discuss the compo-nents of our envisioned system. 1.
Towards Efficient Processing of RDF Data Streams- Short Paper
"... Abstract. In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful informa-tion from these streams of data is essential for some application areas and requires proc ..."
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Abstract. In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful informa-tion from these streams of data is essential for some application areas and requires processing systems that scale to varying conditions in data sources, complex queries, and system failures. This paper describes on-going research on the development of a scalable RDF streaming engine.
