Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
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DOI：
10.1145/3543507.3583370
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发表时间：
2023-03
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通讯作者：
Kota Nakamura;Yasuko Matsubara;Koki Kawabata;Y. Umeda;Yuichiro Wada;Yasushi Sakurai
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作者：
Kota Nakamura;Yasuko Matsubara;Koki Kawabata;Y. Umeda;Yuichiro Wada;Yasushi Sakurai
给定一个巨大的、具有多个属性的时间演化事件的在线流，例如在线购物日志：（商品、价格、品牌、时间），我们如何总结大型的、动态的高阶张量流？我们怎么能看到任何隐藏的模式，规则和异常？我们的答案是关注两种类型的模式，即，“政权”和“组件”，在高阶张量流，我们提出了一个高效和有效的方法，即立方体范围。具体地说，它识别任何突然的不连续性，并识别不同的动态模式，“制度”（例如，工作日/周末/假日模式）。在每种机制中，它还对所有属性执行多路汇总（例如，项目、价格、品牌和时间）并发现表示潜在组的隐藏“组件”（例如，产品/品牌组）及其关系。由于其简洁而有效的总结，CubeScope还可以检测异常的突然出现，并识别实际中发生的异常类型。我们提出的方法具有以下特性：（a）有效：它捕获动态多方面模式，即，制度和组件，并统计总结了所有的事件;（B）一般性：它是实用的成功应用到数据压缩，模式发现，和异常检测的各种类型的张量流;（c）可扩展的：我们的算法不依赖于数据流的长度和它的维数。在真实的数据集上进行的大量实验表明，CubeScope可以正确地发现有意义的模式和异常，并且在准确性和执行速度方面始终优于最先进的方法。
Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), how can we summarize large, dynamic high-order tensor streams? How can we see any hidden patterns, rules, and anomalies? Our answer is to focus on two types of patterns, i.e., “regimes” and “components”, over high-order tensor streams, for which we present an efficient and effective method, namely CubeScope. Specifically, it identifies any sudden discontinuity and recognizes distinct dynamical patterns, “regimes” (e.g., weekday/weekend/holiday patterns). In each regime, it also performs multi-way summarization for all attributes (e.g., item, price, brand, and time) and discovers hidden “components” representing latent groups (e.g., item/brand groups) and their relationship. Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice. Our proposed method has the following properties: (a) Effective: it captures dynamical multi-aspect patterns, i.e., regimes and components, and statistically summarizes all the events; (b) General: it is practical for successful application to data compression, pattern discovery, and anomaly detection on various types of tensor streams; (c) Scalable: our algorithm does not depend on the length of the data stream and its dimensionality. Extensive experiments on real datasets demonstrate that CubeScope finds meaningful patterns and anomalies correctly, and consistently outperforms the state-of-the-art methods as regards accuracy and execution speed.