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Notification of data-stream events in Publish/Subscribe systems using fuzzy matching

机译:使用模糊匹配通知发布/订阅系统中的数据流事件

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摘要

Data streams are vast sources of information. In such streams only certain episodic points called events are interesting. The number of events occurring in a data stream can be very large, and a given user would not be interested in all of them. Thus, there is a need to selectively disseminate information about the occurrence of events to users who express interest in them. Publish/Subscribe is a distributed paradigm of messaging in which events published by publishers are automatically sent to subscribers who have expressed interest in them. In this work, we apply the publish/subscribe paradigm to notify a user when an event of interest occurs. Furthermore, user subscriptions may often be vague and non-specific. To capture this uncertainty, we use fuzzy set theory in the modeling of the subscriptions. The use of fuzzy sets gives a user more expressive power in subscriptions and gives better notifications of events.
机译:数据流是信息的巨大来源。在这样的流中,只有某些称为事件的情节是有趣的。数据流中发生的事件数量可能非常大,并且给定的用户对所有事件都不感兴趣。因此,需要有选择地向对事件表示兴趣的用户传播关于事件发生的信息。发布/订阅是一种消息传递的分布式范例,其中,由发布者发布的事件会自动发送给对此表达了兴趣的订阅者。在这项工作中,我们应用发布/订阅范式在感兴趣的事件发生时通知用户。此外,用户订阅通常可能是模糊且非特定的。为了捕获这种不确定性,我们在订阅的建模中使用模糊集理论。模糊集的使用使用户在订阅中更具表现力,并且可以更好地通知事件。

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