research-article

Taming velocity and variety simultaneously in big data with stream reasoning: tutorial

Authors:

Emanuele Della Valle,

Daniele Dell'Aglio,

Alessandro MargaraAuthors Info & Claims

DEBS '16: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems

Pages 394 - 401

https://doi.org/10.1145/2933267.2933539

Published: 13 June 2016 Publication History

Abstract

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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Cited By

Bonte PTurck FOngenae F(2022)Bridging the gap between expressivity and efficiency in stream reasoning: a structural caching approach for IoT streamsKnowledge and Information Systems10.1007/s10115-022-01686-564:7(1781-1815)Online publication date: 6-Jun-2022
https://doi.org/10.1007/s10115-022-01686-5
Tommasini RBonte PSpiga FDella Valle ETommasini RBonte PSpiga FDella Valle E(2022)General IntroductionStreaming Linked Data10.1007/978-3-031-15371-6_1(1-16)Online publication date: 22-Aug-2022
https://doi.org/10.1007/978-3-031-15371-6_1
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Index Terms

Taming velocity and variety simultaneously in big data with stream reasoning: tutorial
1. Information systems
  1. Data management systems

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cover image ACM Conferences

DEBS '16: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems

June 2016

456 pages

ISBN:9781450340212

DOI:10.1145/2933267

General Chairs:
Avigdor Gal
Technion, Israel
,
Matthias Weidlich
Humboldt-Universität zu Berlin, Germany
,
Program Chairs:
Vana Kalogeraki
Athens University of Economics and Business, Greece
,
Nalini Venkasubramanian
University of California, Irvine

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Published: 13 June 2016

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https://doi.org/10.1007/s10115-022-01686-5
Tommasini RBonte PSpiga FDella Valle ETommasini RBonte PSpiga FDella Valle E(2022)General IntroductionStreaming Linked Data10.1007/978-3-031-15371-6_1(1-16)Online publication date: 22-Aug-2022
https://doi.org/10.1007/978-3-031-15371-6_1
Mach-Król MHadasik B(2021)On a Certain Research Gap in Big Data Mining for Customer InsightsApplied Sciences10.3390/app1115699311:15(6993)Online publication date: 29-Jul-2021
https://doi.org/10.3390/app11156993
Bonte PDe Turck FOngenae F(2021)Towards an Evaluation Framework for Expressive Stream ReasoningThe Semantic Web: ESWC 2021 Satellite Events10.1007/978-3-030-80418-3_14(76-81)Online publication date: 21-Jul-2021
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