Smooth Graphs for Visual Exploration of Higher-Order State Transitions
Smooth Graphs for Visual Exploration of Higher-Order State Transitions
复制标题
用于高阶状态转换可视化探索的平滑图
DOI：
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发表时间：
2009
影响因子：
5.2
通讯作者：
F. Post
中科院分区：
文献类型：
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作者：
Jorik Blaas;C. Botha;Edward Grundy;Mark W. Jones;R. Laramee;F. Post
在本文中，我们提出了一种在大观测时间序列中探索状态序列的新的视觉方法。我们的方法的一个关键优势是它可以直接可视化高阶状态转换。标准的一阶状态转换是由一个转换连接起来的两个状态的序列。高阶状态转换是由三个或更多状态组成的序列，其中参与状态的序列通过连续的一阶状态转换连接在一起。我们的方法通过使用二维图扩展了当前的状态图探索方法，其中高阶状态转换被可视化为曲线。所有的过渡都被捆绑到粗样条中，因此边缘的厚度表示实例的频率。两个状态之间的绑定考虑了状态转换前后的状态转换。这是通过这样一种方式完成的，它形成了一个连续的表示，其中时间序列的任何子序列都由一条连续的光滑线表示。这些图中的边束可以通过我们的增量选择算法进行交互探索。我们通过一个应用程序来展示我们的方法，该应用程序用于探索来自生物调查的标记时间序列数据，其中聚类在每个时间点为数据分配单个标签。在这些序列中，出现了大量的循环模式，而这些循环模式又与特定的活动相关联。我们演示了我们的方法如何帮助发现这些循环，以及交互式选择过程如何帮助发现和调查活动。
In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the timeseries is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm. We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities.