research-article

Uncertainty-Aware Event Analytics over Distributed Settings

Authors:

Nikos Giatrakos,

Alexander Artikis,

Antonios Deligiannakis,

Minos GarofalakisAuthors Info & Claims

DEBS '19: Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems

Pages 175 - 186

https://doi.org/10.1145/3328905.3329763

Published: 24 June 2019 Publication History

Abstract

In complex event processing (CEP), simple derived event tuples are combined in pattern matching procedures to derive complex events (CEs) of interest. Big Data applications analyze event streams online and extract CEs to support decision making procedures. At massive scale, such applications operate over distributed networks of sites where efficient CEP requires reducing communication as much as possible. Besides, events often encompass various types of uncertainty. Therefore, massively distributed Big event Data applications in a world of uncertain events call for communication-efficient, uncertainty-aware CEP solutions, which is the focus of this work. As a proof-of-concept for the applicability of our techniques, we show how we bridge the gap between two recent CEP prototypes which use the same CEP engine and each extend it towards only one of the dimensions of distribution and uncertainty.

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Cited By

Kougioumtzi EKontaxakis ADeligiannakis AKotidis YMargara AValle E(2021)Towards creating a generalized complex event processing operator using FlinkCEPProceedings of the 15th ACM International Conference on Distributed and Event-based Systems10.1145/3465480.3467841(188-189)Online publication date: 28-Jun-2021
https://doi.org/10.1145/3465480.3467841
Giatrakos NDeligiannakis ABereta KVodas MZissis DAlevizos EAkasiadis CArtikis A(2021)Processing Big Data in Motion: Core Components and System Architectures with Applications to the Maritime DomainTechnologies and Applications for Big Data Value10.1007/978-3-030-78307-5_22(497-518)Online publication date: 1-Jul-2021
https://doi.org/10.1007/978-3-030-78307-5_22

Index Terms

Uncertainty-Aware Event Analytics over Distributed Settings

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cover image ACM Conferences

DEBS '19: Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems

June 2019

291 pages

ISBN:9781450367943

DOI:10.1145/3328905

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 June 2019

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DEBS '19

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DEBS '19: The 13th ACM International Conference on Distributed and Event-based Systems

June 24 - 28, 2019

Darmstadt, Germany

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DEBS '19 Paper Acceptance Rate 13 of 47 submissions, 28%;

Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

Kougioumtzi EKontaxakis ADeligiannakis AKotidis YMargara AValle E(2021)Towards creating a generalized complex event processing operator using FlinkCEPProceedings of the 15th ACM International Conference on Distributed and Event-based Systems10.1145/3465480.3467841(188-189)Online publication date: 28-Jun-2021
https://doi.org/10.1145/3465480.3467841
Giatrakos NDeligiannakis ABereta KVodas MZissis DAlevizos EAkasiadis CArtikis A(2021)Processing Big Data in Motion: Core Components and System Architectures with Applications to the Maritime DomainTechnologies and Applications for Big Data Value10.1007/978-3-030-78307-5_22(497-518)Online publication date: 1-Jul-2021
https://doi.org/10.1007/978-3-030-78307-5_22

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