Querying RDF streams with C-SPARQL

Barbieri, Davide; Braga, Daniele; Ceri, Stefano; Valle, Emanuele Della; Grossniklaus, Michael

doi:10.1145/1860702.1860705

Cited by 139 publications

(85 citation statements)

References 30 publications

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“…We compared our approach, Eywa RSP, against state-of-the-art native RSP streaming engines CQELS [11] and C-SPARQL [2]. For each query, we varied the amount of concurrent queries and the number of data streams.…”

Section: Experimentation Evaluating Eywamentioning

confidence: 99%

“…There has been previous work on RSP with the C-SPARQL [2] engine that supports continuous pull-based queries over RDF data streams by using Esper 15 , a complex event processing engine, to form windows in which SPARQL queries can be executed on an in-memory RDF model. Another engine is CQELS [11], which is a purely native RSP, supporting both push and pull queries.…”

Section: Related Workmentioning

confidence: 99%

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Ewya: An Interoperable Fog Computing Infrastructure with RDF Stream Processing

2017

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Abstract. Fog computing is an emerging technology for the Internet of Things (IoT) that aims to support processing on resource-constrained distributed nodes in between the sensors and actuators on the ground and compute clusters in the cloud. Fog Computing benefits from low latency, location awareness, mobility, wide-spread deployment and geographical distribution at the edge of the network. However, there is a need to investigate, optimise for and measure the performance, scalability and interoperability of resource-constrained Fog nodes running real-time applications and queries on streaming IoT data before we can realise these benefits. With Eywa, a novel Fog Computing infrastructure, we 1) formally define and implement a means of distribution and control of query workload with an inverse publish-subscribe and push mechanism, 2) show how data can be integrated and made interoperable through organising data as Linked Data in the Resource Description Format (RDF), 3) test if we can improve RDF Stream Processing query performance and scalability over state-of-the-art engines with our approach to query translation and distribution for a published IoT benchmark on resource-constrained nodes and 4) position Fog Computing within the Internet of the Future.

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Section: Experimentation Evaluating Eywamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Ewya: An Interoperable Fog Computing Infrastructure with RDF Stream Processing

2017

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“…their internal clock can be externally controlled [9]. [7] does. The DSMS produces a snapshot of the active window content at each cycle and the reasoner materializes it from scratch (according to the ontology T and the entailment regime); then all the queries in Q are applied to the new materialization to generate the responses.…”

Section: Baseline Rsp Enginesmentioning

confidence: 99%

Heaven: A Framework for Systematic Comparative Research Approach for RSP Engines

Tommasini

Valle

Balduini

et al. 2016

The Semantic Web. Latest Advances and New Domains

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Abstract. Benchmarks like LSBench, SRBench, CSRBench and, more recently, CityBench satisfy the growing need of shared datasets, ontologies and queries to evaluate window-based RDF Stream Processing (RSP) engines. However, no clear winner emerges out of the evaluation. In this paper, we claim that the RSP community needs to adopt a Systematic Comparative Research Approach (SCRA) if it wants to move a step forward. To this end, we propose a framework that enables SCRA for window based RSP engines. The contributions of this paper are: (i) the requirements to satisfy for tools that aim at enabling SCRA; (ii) the architecture of a facility to design and execute experiment guaranteeing repeatability, reproducibility and comparability; (iii) Heaven -a proof of concept implementation of such architecture that we released as open source -; (iv) two RSP engine implementations, also open source, that we propose as baselines for the comparative research (i.e., they can serve as terms of comparison in future works). We prove Heaven effectiveness using the baselines by: (i) showing that top-down hypothesis verification is not straight forward even in controlled conditions and (ii) providing examples of bottom-up comparative analysis.

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“…Furthermore, there are approaches for querying RDF streams with SPARQL such as Continous SPARQL (C-SPARQL) [3] and Streaming SPARQL [6]. Both represent adaptions of SPARQL which enable the observation of RDF stream windows (most recent triples of RDF streams).…”

Section: Related Workmentioning

confidence: 99%

LODStats – An Extensible Framework for High-Performance Dataset Analytics

Auer

Demter

Martin

et al. 2012

Lecture Notes in Computer Science

116

102

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Abstract. One of the major obstacles for a wider usage of web data is the difficulty to obtain a clear picture of the available datasets. In order to reuse, link, revise or query a dataset published on the Web it is important to know the structure, coverage and coherence of the data. In order to obtain such information we developed LODStats -a statement-stream-based approach for gathering comprehensive statistics about datasets adhering to the Resource Description Framework (RDF). LODStats is based on the declarative description of statistical dataset characteristics. Its main advantages over other approaches are a smaller memory footprint and significantly better performance and scalability. We integrated LODStats with the CKAN dataset metadata registry and obtained a comprehensive picture of the current state of a significant part of the Data Web.

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Querying RDF streams with C-SPARQL

Cited by 139 publications

References 30 publications

Ewya: An Interoperable Fog Computing Infrastructure with RDF Stream Processing

Ewya: An Interoperable Fog Computing Infrastructure with RDF Stream Processing

Heaven: A Framework for Systematic Comparative Research Approach for RSP Engines

LODStats – An Extensible Framework for High-Performance Dataset Analytics

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