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題名 Mining Serial Episode Rules with Time Lags over Multiple Data Streams
作者 陳良弼
Lee, Tung-Ying
Wang, En Tzu
貢獻者 資訊科學系
日期 2008
上傳時間 31-一月-2019 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
資料類型 book/chapter
DOI https://doi.org/10.1007/978-3-540-85836-2_22
dc.contributor 資訊科學系zh_TW
dc.creator (作者) 陳良弼zh_TW
dc.creator (作者) Lee, Tung-Yingen_US
dc.creator (作者) Wang, En Tzuen_US
dc.date (日期) 2008
dc.date.accessioned 31-一月-2019 13:53:47 (UTC+8)-
dc.date.available 31-一月-2019 13:53:47 (UTC+8)-
dc.date.issued (上傳時間) 31-一月-2019 13:53:47 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/122239-
dc.description.abstract (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.en_US
dc.format.extent 450836 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) International Conference on Data Warehousing and Knowledge Discovery
DaWaK 2008: Data Warehousing and Knowledge Discovery pp 227-240		en_US
dc.title (題名) Mining Serial Episode Rules with Time Lags over Multiple Data Streamsen_US
dc.type (資料類型) book/chapter
dc.identifier.doi (DOI) 10.1007/978-3-540-85836-2_22
dc.doi.uri (DOI) https://doi.org/10.1007/978-3-540-85836-2_22