TY - GEN
T1 - The DEBS 2022 Grand Challenge
T2 - 16th ACM International Conference on Distributed and Event-Based Systems, DEBS 2022
AU - Frischbier, Sebastian
AU - Tahir, Jawad
AU - Doblander, Christoph
AU - Hormann, Arne
AU - Mayer, Ruben
AU - Jacobsen, Hans Arno
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/27
Y1 - 2022/6/27
N2 - The DEBS Grand Challenge (GC) is an annual programming competition open to practitioners from both academia and industry. The GC 2022 edition focuses on real-time complex event processing of high-volume tick data provided by Infront Financial Technology GmbH. The goal of the challenge is to efficiently compute specific trend indicators and detect patterns in these indicators like those used by real-life traders to decide on buying or selling in financial markets. The data set Trading Data used for benchmarking contains 289 million tick events from approximately 5500+ financial instruments that had been traded on the three major exchanges Amsterdam (NL), Paris (FR), and Frankfurt am Main (GER) over the course of a full week in 2021. The data set is made publicly available. In addition to correctness and performance, submissions must explicitly focus on reusability and practicability. Hence, participants must address specific nonfunctional requirements and are asked to build upon open-source platforms. This paper describes the required scenario and the data set Trading Data, defines the queries of the problem statement, and explains the enhancements made to the evaluation platform Challenger that handles data distribution, dynamic subscriptions, and remote evaluation of the submissions.
AB - The DEBS Grand Challenge (GC) is an annual programming competition open to practitioners from both academia and industry. The GC 2022 edition focuses on real-time complex event processing of high-volume tick data provided by Infront Financial Technology GmbH. The goal of the challenge is to efficiently compute specific trend indicators and detect patterns in these indicators like those used by real-life traders to decide on buying or selling in financial markets. The data set Trading Data used for benchmarking contains 289 million tick events from approximately 5500+ financial instruments that had been traded on the three major exchanges Amsterdam (NL), Paris (FR), and Frankfurt am Main (GER) over the course of a full week in 2021. The data set is made publicly available. In addition to correctness and performance, submissions must explicitly focus on reusability and practicability. Hence, participants must address specific nonfunctional requirements and are asked to build upon open-source platforms. This paper describes the required scenario and the data set Trading Data, defines the queries of the problem statement, and explains the enhancements made to the evaluation platform Challenger that handles data distribution, dynamic subscriptions, and remote evaluation of the submissions.
KW - Event processing
KW - data streaming
KW - technical analysis
KW - trading
UR - http://www.scopus.com/inward/record.url?scp=85135475452&partnerID=8YFLogxK
U2 - 10.1145/3524860.3539645
DO - 10.1145/3524860.3539645
M3 - Conference contribution
AN - SCOPUS:85135475452
T3 - DEBS 2022 - Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems
SP - 132
EP - 138
BT - DEBS 2022 - Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems
A2 - Zhou, Yongluan
A2 - Chrysanthis, Panos K.
A2 - Gulisano, Vincenzo
A2 - Zacharatou, Eleni Tzirita
PB - Association for Computing Machinery, Inc
Y2 - 27 June 2022 through 30 June 2022
ER -