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.
翻译:DEBS Grand Challenge(GC)是每年向学术界和业界从业人员开放的方案制定竞赛。GC 2022版侧重于对Infront Financial Technology GmbH提供的大量数据进行实时复杂处理。挑战的目标是高效率地计算具体的趋势指标,并发现这些指标的模式,如实际贸易商用来决定在金融市场买卖的指标。基准使用的数据集贸易数据包含大约5500年以上金融工具交易的289 000万计数事件。在2021年整整一周内,阿姆斯特丹(NL)、巴黎(FR)和美因河畔法兰克福(GER)三大交易所进行了交易。数据集公布。数据集除了准确性和业绩外,还必须明确侧重于可重复性和可行性。因此,参与者必须解决具体的非功能性要求,并被要求在开放源平台的基础上建立数据库。本文件描述了所需的情景和数据集贸易数据,界定了对问题陈述的查询,并解释了对处理数据分发、动态订阅和远程评估提交材料的评价平台所作的改进。