Cited By
View all- Kumar SAgarwal S(2024) Rule based complex event processing for IoT applications: Review, classification and challenges Expert Systems10.1111/exsy.13597Online publication date: 30-Mar-2024
Real-time analysis of market data leveraging Apache Flink
Pages 162 - 165
Abstract
In this paper, we present a solution to the DEBS 2022 Grand Challenge (GC). According to the GC requirements, the proposed software continuously observes notifications about financial instruments being traded, aiming to timely detect breakout patterns. Our solution leverages Apache Flink, an open-source, scalable stream processing platform, which allows us to process incoming data streams with low latency and exploit the parallelism offered by the underlying computing infrastructure.
References
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Apache Flink. 2022. Apache Flink Documentation. https://nightlies.apache.org/flink/flink-docs-stable/
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Paris Carbone, Stephan Ewen, Gyula Fóra, Seif Haridi, Stefan Richter, and Kostas Tzoumas. 2017. State Management in Apache Flink: Consistent Stateful Distributed Stream Processing. Proceedings of the VLDB Endowment 10, 12 (2017), 1718--1729.
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Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas. 2015. Apache Flink: Stream and Batch Processing in a Single Engine. IEEE Database Engineering Bulletin 38, 4 (2015), 28--38. http://sites.computer.org/debull/A15dec/p28.pdf
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J. Chen. 2022. The Anatomy of Trading Breakouts. https://www.investopedia.com/articles/trading/08/trading-breakouts.asp
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Sebastian Frischbier, Mario Paic, Alexander Echler, and Christian Roth. 2019. Managing the Complexity of Processing Financial Data at Scale - An Experience Report. In Complex Systems Design & Management. Springer International Publishing, Cham, Switzerland, 14--26.
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Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In Proceedings of the 16th ACM International Conference on Distributed and Event-based Systems, DEBS '22. ACM, New York, NY, USA.
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Nicolo Rivetti, Yann Busnel, and Avigdor Gal. 2017. FlinkMan: Anomaly Detection in Manufacturing Equipment with Apache Flink: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-Based Systems, DEBS '17. ACM, New York, NY, USA, 274--279.
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Gengtao Xu, Jing Qin, and Runqi Tian. 2020. Optimized Parallel Implementation of Sequential Clustering-Based Event Detection. In Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems, DEBS '20. ACM, New York, NY, USA, 208--213.
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Index Terms
- Real-time analysis of market data leveraging Apache Flink
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Published In
June 2022
210 pages
ISBN:9781450393089
DOI:10.1145/3524860
- General Chair:
- Yongluan Zhou,
- Program Chairs:
- Panos K. Chrysanthis,
- Vincenzo Gulisano
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Publication History
Published: 15 July 2022
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- Short-paper
Conference
DEBS '22: The 16th ACM International Conference on Distributed and Event-based Systems
June 27 - 30, 2022
Copenhagen, Denmark
Acceptance Rates
DEBS '22 Paper Acceptance Rate 10 of 19 submissions, 53%;
Overall Acceptance Rate 145 of 583 submissions, 25%
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View all- Kumar SAgarwal S(2024) Rule based complex event processing for IoT applications: Review, classification and challenges Expert Systems10.1111/exsy.13597Online publication date: 30-Mar-2024
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