{"title":"Event Mining and Pattern Discovery","authors":"","doi":"10.1145/3462257.3462261","DOIUrl":null,"url":null,"abstract":"time. Increasingly autonomous systems are being developed to make these sensi tive decisions in critical situations. Big data has ushered in a clear departure from the earlier use of data streams, which was primarily for understanding consumer behavior or for helping people to purchase stocks. An implicit assumption in data systems in the last century was that objects are primary and events are just the properties of an object. In contrast, this century’s data systems place events at the same level as objects. What ancient philosophers [Casati and Varzi 2015] believed about the world being represented by objects and events is finally coming to computers and cyberspaces. With the increasing avail ability of sensor data streams that represent diverse attributes for objects and locations, events are becoming as important as objects. As discussed in Chapter 2, an event has multiple properties, usually recognized at different levels of granularity, and represented with an event model. Depending on the complexity of an application, the event model might either contain all facets (i.e., informational, structural, experiential, spatial, temporal, and causal), or only a few of them. In its simplest form, however, an event model must contain infor mational and temporal facets: the event type or name is needed as a humanand machine-understandable label, and a timestamp is needed because events occur at a certain moment in time or span an interval. Event streams have two main dimensions: (1) temporal sequence, where data are indexed by time, and (2) infor mational segment, where data are encapsulated in events’ properties (such as type, name, location, participants, etc.). As shown in Figure 2.3, event streams contain a Event Mining and Pattern Discovery","PeriodicalId":208013,"journal":{"name":"Event Mining for Explanatory Modeling","volume":"245 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Event Mining for Explanatory Modeling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3462257.3462261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
time. Increasingly autonomous systems are being developed to make these sensi tive decisions in critical situations. Big data has ushered in a clear departure from the earlier use of data streams, which was primarily for understanding consumer behavior or for helping people to purchase stocks. An implicit assumption in data systems in the last century was that objects are primary and events are just the properties of an object. In contrast, this century’s data systems place events at the same level as objects. What ancient philosophers [Casati and Varzi 2015] believed about the world being represented by objects and events is finally coming to computers and cyberspaces. With the increasing avail ability of sensor data streams that represent diverse attributes for objects and locations, events are becoming as important as objects. As discussed in Chapter 2, an event has multiple properties, usually recognized at different levels of granularity, and represented with an event model. Depending on the complexity of an application, the event model might either contain all facets (i.e., informational, structural, experiential, spatial, temporal, and causal), or only a few of them. In its simplest form, however, an event model must contain infor mational and temporal facets: the event type or name is needed as a humanand machine-understandable label, and a timestamp is needed because events occur at a certain moment in time or span an interval. Event streams have two main dimensions: (1) temporal sequence, where data are indexed by time, and (2) infor mational segment, where data are encapsulated in events’ properties (such as type, name, location, participants, etc.). As shown in Figure 2.3, event streams contain a Event Mining and Pattern Discovery
时间。越来越多的自主系统被开发出来,以便在危急情况下做出这些敏感的决定。大数据带来了与早期数据流使用的明显背离,早期数据流主要用于理解消费者行为或帮助人们购买股票。在上个世纪的数据系统中,一个隐含的假设是,对象是主要的,事件只是对象的属性。相反,本世纪的数据系统将事件与对象放在同一层次。古代哲学家(Casati and Varzi 2015)认为世界是由物体和事件代表的,这一观点最终出现在了计算机和网络空间中。随着表示物体和位置的不同属性的传感器数据流的日益可用性,事件变得与物体一样重要。正如第2章所讨论的,一个事件有多个属性,通常在不同的粒度级别上识别,并用事件模型表示。根据应用程序的复杂性,事件模型可能包含所有方面(即信息方面、结构方面、经验方面、空间方面、时间方面和因果方面),也可能只包含其中的几个方面。然而,在其最简单的形式中,事件模型必须包含信息和时间方面:事件类型或名称需要作为人类和机器可理解的标签,并且需要时间戳,因为事件发生在时间的某个时刻或跨越一个间隔。事件流有两个主要维度:(1)时间序列,其中数据按时间索引;(2)国际段,其中数据封装在事件属性中(如类型、名称、位置、参与者等)。如图2.3所示,事件流包含事件挖掘和模式发现