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Event Composition and Detection in Data Stream Management Systems

机译:数据流管理系统中的事件组成和检测

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There has been a rising need to handle and process streaming kind of data. It is continuous, unpredictable, time-varying in nature and could arrive in multiple rapid streams. Sensor data, web clickstreams, etc. are the examples of streaming data. One of the important issues about streaming data management systems is that it needs to be processed in real-time. That is, active rules can be defined over data streams for making the system reactive. These rules are triggered based on the events detected on the data stream, or events detected while summarizing the data or combination of both. In this paper, we study the challenges involved in monitoring events in a Data Stream Management System (DSMS) and how they differ from the same in active databases. We propose an architecture for event composition and detection in a DSMS, and then discuss an algorithm for detecting composite events defined on both the summarized data streams and the streaming data.
机译:需要一个不断上升,处理和处理流式数据的数据。它是连续,不可预测的,在自然中时变,可以到达多个快速流。传感器数据,Web Clickstream等是流数据的示例。关于流媒体数据管理系统的重要问题之一是它需要实时处理。也就是说,可以通过数据流来定义活动规则,以使系统无限制。基于在数据流上检测到的事件或在总结数据或两者的组合时检测到的事件来触发这些规则。在本文中,我们研究了数据流管理系统(DSMS)中监视事件所涉及的挑战以及如何在活动数据库中与相同的不同。我们提出了一种在DSMS中进行事件组合和检测的架构,然后讨论用于检测概述数据流和流数据上定义的复合事件的算法。

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