W. N. Robinson, Ali Raza Syed, A. Akhlaghi, Tianjie Deng
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引用次数: 11
Abstract
We demonstrate the use of sequence pattern mining as applied to monitoring the usage of emailing software by clients with cognitive impairments. We show how Max Motif, a sequence-mining algorithm, can be applied using a stream-mining approach. Consequently, clinicians can now consider sequential patterns in their analysis of client emailing behaviors. Such analysis is part of the Think and Link project, which provides personalized email clients, a kind of assistive technology, to aid client communications that facilitate activities of daily living. By using the simplified, customized system, clients can now email, whereas they could not previously. By continuously monitoring usage, the project is able continually adapt the software to a user's changing needs. Thus, monitoring software usage, particularly email event sequences, is important. This paper introduces theory and design of stream sequence-mining for UI event streams.
我们演示了将序列模式挖掘应用于监控有认知障碍的客户对电子邮件软件的使用。我们展示了如何使用流挖掘方法应用序列挖掘算法Max Motif。因此,临床医生现在可以在分析客户的电子邮件行为时考虑顺序模式。这种分析是Think and Link项目的一部分,该项目提供个性化的电子邮件客户端,这是一种辅助技术,帮助客户沟通,促进日常生活活动。通过使用简化的定制系统,客户现在可以发送电子邮件,而以前则不能。通过持续监控使用情况,项目能够不断调整软件以适应用户不断变化的需求。因此,监控软件的使用,特别是电子邮件事件序列,是很重要的。本文介绍了面向UI事件流的流序列挖掘的原理和设计。