YABench: A Comprehensive Framework for RDF Stream Processor Correctness and Performance Assessment

Kolchin, Maxim; Wetz, Peter; Kiesling, Elmar; Tjoa, A. Min

doi:10.1007/978-3-319-38791-8_16

Cited by 14 publications

(6 citation statements)

References 16 publications

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“…All centralized experiments were carried out on a machine running Linux, which has 4 CPUs with 6 cores and 64GB memory, and 5 machines with the same performance for distributed experiments. For evaluation, we utilized YABench RSP benchmark [6], which uses a real world dataset about water temperature. In our experiments, we performed sliding windows with a window size and a step size of 5 seconds, respectively.…”

Section: Methodsmentioning

confidence: 99%

An Adaptive Framework for RDF Stream Processing

Zhang

Feng

2017

Web and Big Data

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In this paper, we propose a plugin-based framework for RDF stream processing named PRSP. Within this framework, we can employ SPARQL query engines to process C-SPARQL queries with maintaining the high performance of those engines in a simple way. Taking advantage of PRSP, we can process large-scale RDF streams in a distributed context via distributed SPARQL engines. Besides, we can evaluate the performance and correctness of existing SPARQL query engines in handling RDF streams in a united way, which amends the evaluation of them ranging from static RDF (i.e., RDF graph) to dynamic RDF (i.e., RDF stream). Finally, within PRSP, we experimently evaluate the correctness and the performance on YABench. The experiments show that PRSP can still maintain the high performance of those engines in RDF stream processing although there are some slight differences among them.

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Section: Methodsmentioning

confidence: 99%

An Adaptive Framework for RDF Stream Processing

Zhang

Feng

2017

Web and Big Data

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“…Comparable Research & Benchmarking. Aside developing proof-of-concepts, the SR/RSP communities have focused a lot on Comparative Research (CR) [27,24] and benchmarking [32,22,18,3,26]. CR studies the differences and similarities across SR/RSP approaches.…”

Section: Use Casesmentioning

confidence: 99%

“…The RSP community is also working actively on solving this issue, focusing on (i) designing best practices [27,24] (ii) disseminating the approaches [15], and (ii) developing benchmarks that take correctness and execution semantics into account [3,18]. A recent important result is RSPQL [14], a reference model that unifies existing RSP dialects and the execution semantics of existing RSP engines.…”

Section: Introductionmentioning

confidence: 99%

RSP4J: An API for RDF Stream Processing

Tommasini

Bonte

Ongenae

et al. 2021

Lecture Notes in Computer Science

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The RDF Stream Processing (RSP) community has proposed several models and languages for continuously querying and reasoning over RDF streams over the last decade. They each have their semantics, making them hard to compare. The variety of approaches has fostered both empirical and theoretical research and led to the design of RSPQL, i.e., a unifying model for RSP. However, an RSP API for the development under RSPQL semantics was still missing. RSP community would benefit from an RSP API because it can foster comparable and reproducible research by providing programming abstractions based on RSPQL semantics. Moreover, it can encourage further development and in-use research. Finally, it can stimulate practical activities such as tutorials, lectures, and challenges, e.g., during the Stream Reasoning Workshop.In this paper, we present RSP4J, a flexible API for the development of RSP engines and applications under RSPQL semantics. RSP4J offers all the necessary abstractions required for fast-prototyping of RSP engines under the proposed RSPQL semantics. Users can configure it to reproduce the variety of RSP engine behaviors in a comparable software environment. To promote systematic and comparative research, RSP4J is open-source, provides canonical citation, permanent web identifiers, and a comprehensive user guide for developers.

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“…Two more mature proposals, CityBench [3] and YAbench [77], emerged from those pioneering works. They provide real and synthetic workloads together with software environments to ease the execution of the experiments and the measurement of the quality criteria.…”

Section: Measuring Progressesmentioning

confidence: 99%

Stream reasoning: A survey and outlook

et al. 2017

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Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and in this article we analyse its growth. In the first part, we trace the main results obtained so far, by presenting the most prominent studies. We start by an overview of the most relevant studies developed in the context of semantic web, and then we extend the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: in the second part, we present a set of open challenges and issues that stream reasoning will face in the next future.

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YABench: A Comprehensive Framework for RDF Stream Processor Correctness and Performance Assessment

Cited by 14 publications

References 16 publications

An Adaptive Framework for RDF Stream Processing

An Adaptive Framework for RDF Stream Processing

RSP4J: An API for RDF Stream Processing

Stream reasoning: A survey and outlook

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