research-article

Automatic Learning of Predictive CEP Rules: Bridging the Gap between Data Mining and Complex Event Processing

Authors:

Raef Mousheimish,

Karine ZeitouniAuthors Info & Claims

DEBS '17: Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems

Pages 158 - 169

https://doi.org/10.1145/3093742.3093917

Published: 08 June 2017 Publication History

Abstract

Due to the undeniable advantage of prediction and proactivity, many research areas and industrial applications are accelerating the pace to keep up with data science and predictive analytics. However and due to three well-known facts, the reactive Complex Event Processing (CEP) technology might lag behind when prediction becomes a requirement. 1st fact: The one and only inference mechanism in this domain is totally guided by CEP rules. 2nd fact: The only way to define a CEP rule is by writing it manually with the help of a human expert. 3rd fact: Experts tend to write reactive CEP rules, because and regardless of the level of expertise, it is nearly impossible to manually write predictive CEP rules. Combining these facts together, the CEP is---and will stay--- a reactive computing technique. Therefore in this article, we present a novel data mining-based approach that automatically learns predictive CEP rules. The approach proposes a new learning algorithm where complex patterns from multivariate time series are learned. Then at run-time, a seamless transformation into the CEP world takes place. The result is a ready-to-use CEP engine with enrolled predictive CEP rules. Many experiments on publicly-available data sets demonstrate the effectiveness of our approach.

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Cited By

Wang YWu MLi XXie LChen Z(2024)Multivariate Time-Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural NetworkIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32418965:1(321-333)Online publication date: Jan-2024
https://doi.org/10.1109/TAI.2023.3241896
Michelioudakis EArtikis APaliouras G(2023)Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarityMachine Learning10.1007/s10994-023-06447-1113:3(1445-1481)Online publication date: 15-Dec-2023
https://doi.org/10.1007/s10994-023-06447-1
Alaghbari KSaad MHussain AAlam M(2022)Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendationsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00338-x11:1Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1186/s13677-022-00338-x
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Index Terms

Automatic Learning of Predictive CEP Rules: Bridging the Gap between Data Mining and Complex Event Processing
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Rule learning
2. Information systems
  1. Data management systems
    1. Database management system engines
      1. Stream management
  2. Information systems applications
    1. Data mining

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cover image ACM Conferences

DEBS '17: Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems

June 2017

393 pages

ISBN:9781450350655

DOI:10.1145/3093742

Copyright © 2017 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 the author(s) 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: 08 June 2017

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DEBS '17

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DEBS '17: The 11th ACM International Conference on Distributed and Event-based Systems

June 19 - 23, 2017

Barcelona, Spain

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DEBS '17 Paper Acceptance Rate 22 of 60 submissions, 37%;

Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

Wang YWu MLi XXie LChen Z(2024)Multivariate Time-Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural NetworkIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32418965:1(321-333)Online publication date: Jan-2024
https://doi.org/10.1109/TAI.2023.3241896
Michelioudakis EArtikis APaliouras G(2023)Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarityMachine Learning10.1007/s10994-023-06447-1113:3(1445-1481)Online publication date: 15-Dec-2023
https://doi.org/10.1007/s10994-023-06447-1
Alaghbari KSaad MHussain AAlam M(2022)Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendationsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00338-x11:1Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1186/s13677-022-00338-x
Phan TQuach NNguyen TNguyen TJo JNguyen Q(2022)Real-time wildfire detection with semantic explanationsExpert Systems with Applications10.1016/j.eswa.2022.117007201(117007)Online publication date: Sep-2022
https://doi.org/10.1016/j.eswa.2022.117007
Konovalenko ILudwig A(2022)Generating decision support for alarm processing in cold supply chains using a hybrid k-NN algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.116208190:COnline publication date: 15-Mar-2022
https://dl.acm.org/doi/10.1016/j.eswa.2021.116208
Bruns RDunkel J(2022)Bat4CEP: a bat algorithm for mining of complex event processing rulesApplied Intelligence10.1007/s10489-022-03256-252:13(15143-15163)Online publication date: 11-Mar-2022
https://doi.org/10.1007/s10489-022-03256-2
Geisler S(2022)Complex Event Processing (CEP)Encyclopedia of Big Data10.1007/978-3-319-32010-6_276(192-198)Online publication date: 12-Feb-2022
https://doi.org/10.1007/978-3-319-32010-6_276
Ren HAnicic DRunkler TMargara AValle E(2021)The synergy of complex event processing and tiny machine learning in industrial IoTProceedings of the 15th ACM International Conference on Distributed and Event-based Systems10.1145/3465480.3466928(126-135)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3465480.3466928
Ramírez AMoreno NVallecillo A(2021)Rule‐based preprocessing for data stream mining using complex event processingExpert Systems10.1111/exsy.1276238:8Online publication date: 20-Jul-2021
https://doi.org/10.1111/exsy.12762
Zuo JZeitouni KTaher Y(2021)SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00206(1565-1570)Online publication date: Dec-2021
https://doi.org/10.1109/ICDM51629.2021.00206
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