Automatic and Effective Mining of Coevolving Online Activities
Automatic and Effective Mining of Coevolving Online Activities
复制标题
协同演化在线活动的自动有效挖掘
DOI：
10.1007/978-3-319-57529-2_19
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Yasushi Sakurai
中科院分区：
文献类型：
--
作者：
Thinh Minh Do;Yasuko Matsubara;Yasushi Sakurai
考虑到大量随时间变化的在线用户活动，例如针对各种类别（名人、事件、疾病等）的多个关键字的Google搜索查询，其中包括关键字/活动，为国家/地区的持续时间，我们如何找到模式和规则？我们如何捕捉当地活动的非线性演变并预测未来模式？我们的目标也是实现良好的监测数据序列的统计，并立即检测的模式。本文提出了一个用于分析大规模网络搜索数据的统一的非线性分析模型，该模型是有意义的、自动的、可扩展的和无参数的，并且可以预测关键字/查询的长期未来动态。此外，我们还提供了一个高效和有效的拟合算法，导致新的发现和意义的功能，并有助于监测多个共同发展的数据序列的需要。
Given a large collection of time-evolving online user activities, such as Google Search queries for multiple keywords of various categories (celebrities, events, diseases, etc.), which consist ofkeywords/activities, forcountries/locations of duration, how can we find patterns and rules? How do we go about capturing non-linear evolutions of local activities and forecasting future patterns? We also aim to achieve good monitoring of the data sequences statistically, and detection of the patterns immediately. In this paper, we present, a unifying analytical non-linear model for analysing large scale web search data, which is sense-making, automatic, scalable and free of parameters.can also forecast long-range future dynamics of the keywords/queries. Besides, we also provide an efficient and effective fitting algorithm, which leads to novel discoveries and sense-making features, and contribute to the need of monitoring multiple co-evolving data sequences.
DOI：
10.1145/2872427.2883010
发表时间：
2016-04
期刊：
Proceedings of the 25th International Conference on World Wide Web
影响因子：
--
作者：
Yasuko Matsubara;Yasushi Sakurai;C. Faloutsos
通讯作者：
Yasuko Matsubara;Yasushi Sakurai;C. Faloutsos
影响因子：
2.5
作者：
Li, Lei;Prakash, B. Aditya;Faloutsos, Christos
通讯作者：
Faloutsos, Christos