Novel Circular Graph Capabilities for Comprehensive Visual Analytics of Interconnected Data in Digital Humanities
Novel Circular Graph Capabilities for Comprehensive Visual Analytics of Interconnected Data in Digital Humanities
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用于数字人文中互连数据的综合可视化分析的新颖圆形图功能
DOI：
10.26583/sv.12.4.06
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发表时间：
2020
影响因子：
--
通讯作者：
S. Chuprina
中科院分区：
文献类型：
--
作者：
K. Ryabinin;K. Belousov;S. Chuprina
本文致力于工具的开发，这些工具能够提高互联数据可视化分析的综合能力。这类数据对数字人文领域的研究人员来说是一个巨大的挑战。我们提议使用本体驱动的SciVi可视化分析平台来应对这一挑战，并帮助研究人员将数据活化。所提出的分析组件基于圆形图，将数据元素表示为圆形分布的节点，将数据元素的连接表示为三次抛物线的弧线。SciVi平台不仅提供了用于图形可视化分析的传统交互手段，例如基于正则表达式的节点搜索、鼠标悬停时突出显示关联边和连接节点、用颜色描绘聚类、基于阈值对加权节点和边进行过滤等，还提供了一系列新特性，有助于解决特殊的分析任务。本文介绍了这些新特性以及相应的用例。首先，我们提出一种本体驱动的数据提取、转换和加载机制，该机制允许从不同来源获取输入数据，并通过高级可视化编程语言定义的自定义算法对其进行预处理。其次，我们开发了一种多级环形刻度，它围绕圆形图放置，允许根据给定的分类器对图节点进行分组，并在运行时自动重新排序。第三，我们展示了一种均衡过滤器的实现，它允许对不同组的图节点/边应用不同的过滤阈值，以去除噪声数据。在数据噪声在图中具有不均匀强度分布的情况下，这对于数据整理是必要的。第四，我们开发了一种图状态计算器，它允许通过对图中显示的数据切片执行并集、交集等不同操作来进行数据比较。第五，我们能够使图当前可视化的数据切片与地理地图上相应的局部区域同步。由于所呈现的这些特性，SciVi高级交互工具能够利用数字人文和大数据中可视化分析的力量。
The paper is devoted to the development of tools, which enable to improve the comprehensive power of visual analytics of interconnected data. This kind of data is a great challenge for researchers in the field of Digital Humanities. We propose using ontology-driven SciVi visual analytics platform to tackle this challenge and help researchers to bring data to life. The proposed analytics components are based on the circular graph, representing the data elements as the circle distributed nodes and the data elements’ connections as the cubic parabolas’ arcs. SciVi platform provides not only the traditional interactive means for graph visual analytics, such as node searching based on regular expressions, highlighting of incident edges and connected nodes by mouse hover, depicting clusters by colors, threshold-based filtering of weighted nodes and edges, etc., but also a set of new features, which help to solve special analytics tasks. The paper presents these novel features and corresponding use cases. First, we propose an ontology-driven data extraction, transformation and loading mechanism that allows obtaining the input data from different sources and preprocessing them by custom algorithms defined by means of high-level visual programming language. Second, we developed a multilevel ring scale that is placed around the circular graph allowing to group the graph nodes according to the given classifier and automatically reorder them at runtime. Third, we demonstrate an implementation of the equalizing filter that allows applying different filtering thresholds to different groups of graph nodes/edges to cut off the noisy data. This is necessary for data wrangling in the case the data noise has a non-uniform strength distribution across the graph. Fourth, we developed a graph state calculator that allows data comparison by performing different operations like union, intersection, etc. on the data slices shown within the graph. Fifth, we make it possible to synchronize the data slice currently visualized by the graph with the corresponding localized area on the geographical map. Thanks to the features presented, the SciVi advanced interactive tools can harness the power of visual analytics in Digital Humanities and Big Data.