IEEE Trans Vis Comput Graph. 2017 Jun;23(6):1636-1649. doi: 10.1109/TVCG.2016.2539960. Epub 2016 Mar 9.
The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.
不断增长的数据量和种类为可视分析既带来了机遇，也带来了挑战。要想充分挖掘大数据的价值，为商业、安全、社交媒体和医疗保健领域提供新颖的解决方案，就必须解决这些挑战。在时间序列事件分析中，数据分析面临的挑战是数据中的事件数量和时间序列模式的多样性。本文描述了 15 种用于集中分析的策略，分析师可以使用这些策略来减少数据量和模式多样性。我们提出了四组策略：（1）提取策略；（2）时间折叠；（3）模式简化策略；（4）迭代策略。对于每种策略，我们都提供了使用示例及其对数据量和/或多样性的影响。示例选自我们自己的工作、文献或基于对进行分析的个人和观察分析师使用工具的开发人员的电子邮件访谈。最后，我们讨论了这些策略如何结合使用，并报告了来自 10 位高级事件序列分析师的反馈。
