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

Tommasini, Riccardo; Valle, Emanuele Della; Balduini, Marco; Dell’Aglio, Daniele

doi:10.1007/978-3-319-34129-3_16

Cited by 10 publications

(9 citation statements)

References 28 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%

“…To guarantee repeatability and reproducibility, researchers must have full control over both the experimental environment and the object of study. Thus, our main research contribution to solve the validation problem are (i) a methodology for experiment design for RSP [42] and the architecture for an experimental environment based on the notion of Test-Stand [46], and (ii) a Web environment for experimentation called RSPLab, which guarantees reproducibility and repeatability of experimental results using containerization techniques in the context of RDF Stream Processing. Moreover, in [44], we highlight the issues related to designing a query language based on RSP-QL formalization that treats streams as first-class objects and keeps the constructs minimal, homogeneous, symmetric and orthogonal [15].…”

Section: Major Resultsmentioning

confidence: 99%

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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%

Section: Major Resultsmentioning

confidence: 99%

Velocity on the Web

Tommasini

2021

Special Topics in Information Technology

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“…Third, the long term success of stream reasoning requires a framework for comparative evaluation [64,29]. The community needs a comprehensive and widely accepted benchmark that can be used to provide concrete evidence that stream reasoning is the best solution in some domain.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Taming velocity and variety simultaneously in big data with stream reasoning

Valle

Dell’Aglio

Margara

2016

Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems

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Many "big data" applications must tame velocity (processing data in-motion) and variety (processing many different types of data) simultaneously.The research on knowledge representation and reasoning has focused on the variety of data, devising data representation and processing techniques that promote integration and reasoning on available data to extract implicit information. On the other hand, the event and stream processing community has focused on the velocity of data, producing systems that efficiently operate on streams of data on-the-fly according to pre-deployed processing rules or queries. Several recent works explore the synergy between stream processing and reasoning to fully capture the requirements of modern data intensive applications, thus giving birth to the research domain of stream reasoning.This tutorial paper offers an overview of the theoretical and technological achievements in stream reasoning, highlighting the key benefits and limitations of existing approaches, and discussing the open challenges and the opportunities for future research. The paper mainly targets researchers and practitioners in the area of event and stream processing. The paper aims to stimulate the discussion on stream reasoning and to further promote the integration of reasoning techniques within event and stream processing systems in three ways: (i) by presenting an active research domain, where researchers on event and stream processing can apply their expertise; (ii) by discussing techniques and technologies that can help advancing the state of the art in event and stream processing; (iii) by identifying the open problems in the field of stream reasoning, and drawing attention to promising research directions.

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

Cited by 10 publications

References 28 publications

Velocity on the Web

Velocity on the Web

Taming velocity and variety simultaneously in big data with stream reasoning

Handling Impossible Derivations During Stream Reasoning

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