RSPLab: RDF Stream Processing Benchmarking Made Easy

Tommasini, Riccardo; Valle, Emanuele Della; Mauri, Andrea; Brambilla, Marco

doi:10.1007/978-3-319-68204-4_21

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…The investigation related the validation problem develops in [46][47][48]. Validation research is comparative, and thus, it relies on the notion of experiment [28].…”

Section: Major Resultsmentioning

confidence: 99%

Velocity on the Web

Tommasini

2021

Special Topics in Information Technology

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A new generation of Web Applications is pushing the Web infrastructure to process data as soon as they arrive and before they are no longer valuable. However, the Web infrastructure as it is not adequate, and Stream Processing technologies cannot deal with heterogeneous data streams and events. To solve these issues, we need to investigate how to identify, represent, and process streams and events on the Web. In this chapter, we discuss the recent advancements for taming Velocity on the Web of Data without neglecting Data Variety. Thus, we present a Design Science research investigation that builds on the state of the art of Stream Reasoning and RDF Stream Processing. We present our research results, for representing and processing stream and events on the Web, and we discuss their potential impact.

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“…The investigation related the validation problem develops in [46][47][48]. Validation research is comparative, and thus, it relies on the notion of experiment [28].…”

Section: Major Resultsmentioning

confidence: 99%

Velocity on the Web

Tommasini

2021

Special Topics in Information Technology

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“…Stream Reasoners and RSP engines can only be compared when they employ the same semantics. Thus, a fair comparison demands a deep theoretical comprehension of the approaches, a proper formulation of the task to solve, and an adequate experimentation environment [28]. Consequently, it is currently hard to (C4) reproduce the behavior of existing approaches in a comparable way.…”

Section: Use Casesmentioning

confidence: 99%

“…On the other hand, research on benchmarking aims at pushing the technological progress by guaranteeing a fair assessment. While some of the challenges are shared with CR [27,28], benchmarking is empirical research. To this extent, (C6) monitoring both the execution of continuous queries, as well as (C7) the engine behavior at run-time are of paramount importance.…”

Section: Use Casesmentioning

confidence: 99%

RSP4J: An API for RDF Stream Processing

et al. 2021

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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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“…Since these works support different semantics, it is challenging to compare them. Indeed, providing a fair and comprehensive comparison of the various proposal remains an open problem, despite notable efforts in this direction [30,33,34].…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Handling Impossible Derivations During Stream Reasoning

et al. 2020

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With the rapid expansion of the Web and the advent of the Internet of Things, there is a growing need to design tools for intelligent analytics and decision making on streams of data. Logic-based frameworks like LARS allow the execution of complex reasoning on such streams, but it is paramount that the computation is completed in a timely manner before the stream expires. To reduce the runtime, we can extend the validity of inferred conclusions to the future to avoid repeated derivations, but this is not enough to avoid all sources of redundant computation. To further alleviate this problem, this paper introduces a new technique that infers the impossibility of certain derivations in the future and blocks the reasoner from performing computation that is doomed to fail anyway. An experimental analysis on microbenchmarks shows that our technique leads to a significant reduction of the reasoning runtime.

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RSPLab: RDF Stream Processing Benchmarking Made Easy

Cited by 17 publications

References 13 publications

Velocity on the Web

Velocity on the Web

RSP4J: An API for RDF Stream Processing

Handling Impossible Derivations During Stream Reasoning

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