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    题名: Mining Serial Episode Rules with Time Lags over Multiple Data Streams
    作者: 陳良弼
    Lee, Tung-Ying
    Wang, En Tzu
    贡献者: 資訊科學系
    日期: 2008
    上传时间: 2019-01-31 13:53:47 (UTC+8)
    摘要: The problem of discovering episode rules from static databases has been studied for years due to its wide applications in prediction. In this paper, we make the first attempt to study a special episode rule, named serial episode rule with a time lag in an environment of multiple data streams. This rule can be widely used in different applications, such as traffic monitoring over multiple car passing streams in highways. Mining serial episode rules over the data stream environment is a challenge due to the high data arrival rates and the infinite length of the data streams. In this paper, we propose two methods considering different criteria on space utilization and precision to solve the problem by using a prefix tree to summarize the data streams and then traversing the prefix tree to generate the rules. A series of experiments on real data is performed to evaluate the two methods.
    關聯: International Conference on Data Warehousing and Knowledge Discovery
    DaWaK 2008: Data Warehousing and Knowledge Discovery pp 227-240
    数据类型: book/chapter
    DOI 連結: https://doi.org/10.1007/978-3-540-85836-2_22
    DOI: 10.1007/978-3-540-85836-2_22
    显示于类别:[心理學系] 專書/專書篇章

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