Non-Linear Mining of Competing Local Activities
Non-Linear Mining of Competing Local Activities
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DOI：
10.1145/2872427.2883010
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发表时间：
2016-04
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影响因子：
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通讯作者：
Yasuko Matsubara;Yasushi Sakurai;C. Faloutsos
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文献类型：
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作者：
Yasuko Matsubara;Yasushi Sakurai;C. Faloutsos
假设有大量随时间演变的活动，如谷歌搜索查询，其中包括m个地点的d个关键字/活动，持续时间为n，我们如何分析所有这些活动之间的时间模式和关系，并找到特定地点的趋势？我们如何着手捕捉当地活动的非线性演变并预测未来模式？例如，假设我们有从2004年到2015年236个国家/地区的多个关键字的在线搜索量，例如“Nokia/Nexus/Kindle”或“CNN/BBC”。我们的目标是分析大量多变的活动，特别是回答以下问题：(A)两个不同的关键字之间是否有交互/竞争的迹象？如果是这样的话，谁在和谁竞争？(B)哪个国家的竞争最强？(C)是否有任何季节性/年度活动？(D)我们如何能够自动发现世界范围内(或本地)的重要事件？我们提出了COMPCUBE，这是一个统一的非线性模型，它提供了一个紧凑而强大的协同进化活动的表示，以及一个新的匹配算法，COMPCUBE-Fit，它是一种无参数和可伸缩的算法。我们的方法捕捉到以下重要模式：(B)ASIC趋势，即共同进化活动的非线性动态，(C)重复和潜在互动的迹象，例如诺基亚与Nexus，(S)季节性，例如iPod在美国和欧洲的圣诞节高峰，以及(D)Eltas，例如，不重复的本地事件，如2008年美国大选。由于其简明而有效的摘要，COMPCUBE还可以预测长期的未来活动。在真实数据集上的大量实验表明，COMPCUBE在准确率和执行速度方面始终优于最先进的方法。
Given a large collection of time-evolving activities, such as Google search queries, which consist of d keywords/activities for m locations of duration n, how can we analyze temporal patterns and relationships among all these activities and find location-specific trends? How do we go about capturing non-linear evolutions of local activities and forecasting future patterns? For example, assume that we have the online search volume for multiple keywords, e.g., "Nokia/Nexus/Kindle" or "CNN/BBC" for 236 countries/territories, from 2004 to 2015. Our goal is to analyze a large collection of multi-evolving activities, and specifically, to answer the following questions: (a) Is there any sign of interaction/competition between two different keywords? If so, who competes with whom? (b) In which country is the competition strong? (c) Are there any seasonal/annual activities? (d) How can we automatically detect important world-wide (or local) events? We present COMPCUBE, a unifying non-linear model, which provides a compact and powerful representation of co-evolving activities; and also a novel fitting algorithm, COMPCUBE-FIT, which is parameter-free and scalable. Our method captures the following important patterns: (B)asic trends, i.e., non-linear dynamics of co-evolving activities, signs of (C)ompetition and latent interaction, e.g., Nokia vs. Nexus, (S)easonality, e.g., a Christmas spike for iPod in the U.S. and Europe, and (D)eltas, e.g., unrepeated local events such as the U.S. election in 2008. Thanks to its concise but effective summarization, COMPCUBE can also forecast long-range future activities. Extensive experiments on real datasets demonstrate that COMPCUBE consistently outperforms the best state-of- the-art methods in terms of both accuracy and execution speed.